ElevenLabs Agents vs Bland

Agent ops

1. ai-native userMy coding agent can provision a complete voice agent end to end — create the agent, attach a number, and place a call — through the API, CLI, or MCP without touching the dashboard

   weight 3 · round to Bland
   ElevenLabs Agents~partialprobed6/10

   Docs and runtime probes confirm agents can be created and managed via API, a real CLI (@elevenlabs/cli, verified live on npm), and a hosted MCP server (verified live with OAuth), all without the dashboard (docs-1,3,4,27,28,38; probe-rt-1,2). Phone-number attachment is also supported via SIP trunking to route calls to agents (docs-12,36), but there is no explicit documentation or evidence of actually placing/initiating an outbound call end-to-end through the API/CLI/MCP — only routing inbound infrastructure integration is described. Missing for 10: explicit API/CLI/MCP call to place an outbound call, and a concrete end-to-end walkthrough combining create+attach+call in one non-dashboard flow.

   • [claimed-docs] “ElevenLabs Agents are managed either through the ElevenAgents dashboard, the ElevenLabs API, the Agents CLI or the hosted MCP server.”
   • [claimed-docs] “Use the ElevenLabs agents skill to build and manage voice agents from your AI coding assistant: npx skills add elevenlabs/skills --skill age…”
   • [claimed-docs] “an assistant such as Claude can create, configure, and manage the agents in your workspace through natural language, with nothing to install…”
   • [claimed-docs] “This integration enables all customers to use their existing phone systems while leveraging ElevenLabs' advanced voice AI capabilities.”
   • [claimed-docs] “Route calls to AI agents without changing your existing phone infrastructure”
   • [claimed-docs] “connect the hosted MCP server to Claude or any other MCP client to create and manage agents through natural language”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y @elevenlabs/cli --version` printed `elevenlabs 1.1.0` keylessly — the official ElevenLabs CLI (…”
   • [probe] “PROBE runtime (recorded 2026-09-05): keyless JSON-RPC initialize POST to the hosted MCP server https://api.elevenlabs.io/v1/mcp returned HTT…”

   proof ↗⚑ flag

   Bland✓fullprobed8/10

   Bland's docs show all three provisioning steps available programmatically: pathway/agent creation via API (bland-docs-1, bland-docs-18), phone number acquisition/porting/Twilio and SIP attachment (bland-docs-3, bland-docs-16, bland-docs-17), and call placement via API or batch calls (bland-docs-6, bland-docs-23). Both the CLI ('make calls, build and test pathways, configure phone numbers' — bland-docs-12) and the MCP server ('place and inspect calls, build and validate pathways, manage agents' — bland-docs-11/20/27) explicitly cover the full create-agent/attach-number/place-call lifecycle without the dashboard, and runtime probes confirm both the CLI and hosted MCP endpoint are live and functional (bland-probe-rt-1, bland-probe-rt-2). Missing for 10: a single consolidated end-to-end tutorial/example walking through create→attach→call in one flow, and independent (non-vendor) confirmation of the full pipeline working end-to-end.

   • [claimed-docs] “See the Pathways API reference to create and manage pathways programmatically.”
   • [claimed-docs] “Users can connect their own Twilio account to Bland, easily bring over your existing phone numbers to use within the platform.”
   • [claimed-docs] “Batch calls let you initiate high-volume call campaigns by uploading a list of recipients via CSV.”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “Manage your entire Bland account from the terminal: make calls, build and test pathways, configure phone numbers, and more.”
   • [claimed-docs] “Number porting to bring existing numbers to Bland”
   • [claimed-docs] “Bland supports inbound and outbound SIP. Point calls from your carrier or PBX at Bland to be answered by an agent, or have Bland place calls…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y bland-cli --version` printed `0.6.2` keylessly — the official npm CLI (bland-cli, with dev term…”
   • [probe] “PROBE runtime (recorded 2026-09-05): keyless JSON-RPC initialize POST to the hosted MCP endpoint https://api.bland.ai/v1/mcp returned HTTP 4…”

   proof ↗⚑ flag

2. ai-native userThe platform's own AI helps me author agents — generating or improving prompts, flows, and test cases from a description

   weight 1 · round to ElevenLabs Agents
   ElevenLabs Agents~partialprobed5/10

   ElevenLabs documents that an external AI assistant (e.g., Claude) can create, configure, and manage agents through natural language via the hosted MCP server, and agent-testing docs describe turning real conversations into test cases for regression testing — both are AI-assisted authoring flows. However, this relies on a third-party AI client (Claude) rather than a built-in, platform-native 'describe your agent and we generate it' copilot for prompts/flows within the dashboard itself. Missing for 10: evidence of a first-party generative wizard that authors prompts/flows purely from a natural-language description inside the ElevenLabs UI, and evidence of AI-generated (not just conversation-derived) test-case authoring.

   • [claimed-docs] “an assistant such as Claude can create, configure, and manage the agents in your workspace through natural language, with nothing to install…”
   • [claimed-docs] “connect the hosted MCP server to Claude or any other MCP client to create and manage agents through natural language”
   • [claimed-docs] “Transform real conversations into test cases when you find an interaction where the agent underperformed.”
   • [claimed-docs] “Agent testing lets you verify conversational responses, tool usage, and full multi-turn outcomes before you deploy.”
   • [probe] “PROBE runtime (recorded 2026-09-05): keyless JSON-RPC initialize POST to the hosted MCP server https://api.elevenlabs.io/v1/mcp returned HTT…”

   proof ↗⚑ flag

   Bland~partialclaimed4/10

   Bland's MCP server lets external AI coding agents 'build and validate pathways ... through natural language' (bland-docs-11/20/27), and the testbed/evals tools support iterating on and grading prompts, which loosely supports AI-assisted authoring. However there is no evidence of a built-in, first-party generative feature where Bland's own platform AI drafts a full pathway/flow/test-cases from a plain-language description inside the product itself — the closest capability requires an external AI agent connecting via MCP. Missing for 10: a native 'describe your agent, we generate the pathway/prompts/tests' feature, in-product prompt-improvement AI, and independent/hands-on confirmation of AI-generated flows.

   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The testbed lets you take any call ... isolate a specific node interaction, edit the prompt, and run it multiple times to see how the output…”
   • [claimed-docs] “The testbed lets you take any call — whether it's a historical call from call logs or a test chat from the pathway editor — isolate a specif…”
   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges.”
   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges. You define eval agents, each of which grades one dimension of a call”

   proof ↗⚑ flag

Build

1. developerBuild a working phone voice agent — prompt, voice, and phone number — and take my first live call within an hour

   weight 3 · round to Bland
   ElevenLabs Agents~partialclaimed7/10

   Docs show a clear quickstart path — create an agent via dashboard/API, pick from 5k+ voices, and connect phone numbers via SIP trunking to route calls to the agent without changing existing phone infrastructure — which together cover the prompt+voice+phone number pieces of the story. However, there's no explicit end-to-end 'first call in under an hour' walkthrough or independent timing evidence, and phone-number provisioning specifics (vs. just SIP integration) are thin. Missing for 10: an explicit timed/complete quickstart-to-live-call walkthrough, first-party or independent evidence of the actual time-to-first-call, and clearer native phone-number purchase/setup docs beyond SIP trunking.

   • [claimed-docs] “ElevenLabs Agents are managed either through the ElevenAgents dashboard, the ElevenLabs API, the Agents CLI or the hosted MCP server.”
   • [claimed-docs] “Select from 5k+ voices across 31 languages with customization options”
   • [claimed-docs] “This integration enables all customers to use their existing phone systems while leveraging ElevenLabs' advanced voice AI capabilities.”
   • [claimed-docs] “Route calls to AI agents without changing your existing phone infrastructure”
   • [claimed-docs] “we'll start by creating an agent via the API or the web dashboard”
   • [claimed-docs] “This assistant can be embedded into your website or app to provide real-time support to your customers”

   proof ↗⚑ flag

   Bland✓fullprobed8/10

   Docs cover the whole first-call workflow: creating a pathway/prompt (bland-docs-18, bland-docs-26, bland-docs-30), voice cloning/selection (bland-docs-10), phone number setup via own Twilio or new inbound numbers (bland-docs-3, bland-docs-24), and dispatching outbound calls (bland-docs-23), plus a CLI and MCP server confirmed live at runtime (bland-probe-rt-1, bland-probe-rt-2) that let a developer configure and place calls quickly. Missing for 10: no independent hands-on account of a developer actually completing a first call within an hour, and one community comment notes cost concerns rather than time-to-first-call, so onboarding speed is only documented, not externally verified.

   • [claimed-docs] “Conversational pathways are our new way of prompting Bland that has led to major breakthroughs in realism. Give your agent instructions on h…”
   • [claimed-docs] “Conversational pathways are our new way of prompting Bland that has led to major breakthroughs in realism.”
   • [claimed-docs] “Give your agent instructions on how it should respond at specific points of the conversation. Choose between prompting or fixed sentences. E…”
   • [claimed-docs] “A clone needs one clean sample of about ten seconds. Quality of the sample sets the ceiling on quality of the voice”
   • [claimed-docs] “Users can connect their own Twilio account to Bland, easily bring over your existing phone numbers to use within the platform.”
   • [claimed-docs] “Create inbound phone numbers for customer support, etc.”
   • [claimed-docs] “Dispatch AI phone calls to call customers, leads, and to streamline operations.”
   • [claimed-docs] “The API integration lets you connect your AI agent to any HTTP endpoint.”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y bland-cli --version` printed `0.6.2` keylessly — the official npm CLI (bland-cli, with dev term…”
   • [probe] “PROBE runtime (recorded 2026-09-05): keyless JSON-RPC initialize POST to the hosted MCP endpoint https://api.bland.ai/v1/mcp returned HTTP 4…”

   proof ↗⚑ flag

2. developerRun conversations in multiple languages, including detecting and switching language mid-call

   weight 2 · round to ElevenLabs Agents
   ElevenLabs Agents~partialclaimed4/10

   Docs confirm broad multi-language support (70+ languages, language-specific voice settings) and voice-switching for multi-character use, but there is no explicit documentation of automatic language detection or switching languages mid-call. Missing for 10: explicit language-detection/mid-call switching feature docs, and any hands-on demonstration of a live language switch during a single conversation.

   • [claimed-docs] “Configure, deploy and monitor natural, human-sounding agents in 70+ languages with leading accuracy and ultra-low latency across voice or ch…”
   • [claimed-docs] “You can customize various aspects of your AI agent's voice to create a more natural and engaging conversation experience. This includes cont…”
   • [claimed-docs] “Enable your agent to switch between different voices for multi-character conversations, storytelling, and language tutoring.”
   • [claimed-docs] “Select from 5k+ voices across 31 languages with customization options”

   proof ↗⚑ flag

   Bland—none0/10

   The evidence pack contains no mention of multi-language support, language detection, or mid-call language switching anywhere in Bland's docs; only pathways, TTS voice cloning, and infrastructure features are documented. Missing for 10: any documentation of multilingual conversation support, automatic language detection, or dynamic language switching mid-call.

   ⚑ flag

3. founderDesign multi-step conversation flows in a visual builder with branching, states, and handoffs without writing code

   weight 2 · round to Bland
   ElevenLabs Agents~partialclaimed6/10

   Docs explicitly claim a 'visual workflow builder' for multi-step workflows, plus dashboard-based agent creation, conversation-flow settings (turn-taking, interruptions, timeouts), and tools/handoff-like actions (webhooks, client tools) — supporting the no-code, branching, multi-step story. However, evidence never details branching logic, explicit 'states' or agent-to-agent 'handoffs' within the builder, nor independent hands-on confirmation of the visual builder's depth. Missing for 10: concrete detail/screenshots of branching and state logic in the builder, explicit handoff-between-agents feature description, and independent/third-party corroboration of the builder's no-code sufficiency.

   • [claimed-docs] “Build multi-step workflows with visual workflow builder”
   • [claimed-docs] “Conversation flow settings determine how your assistant handles periods of user silence, interruptions during speech, and turn-taking behavi…”
   • [claimed-docs] “Configure turn-taking, interruptions, and timeout settings”
   • [claimed-docs] “we'll start by creating an agent via the API or the web dashboard”
   • [claimed-docs] “Enable agents to call clients & APIs to perform actions”

   proof ↗⚑ flag

   Bland✓fullclaimed7/10

   Bland's 'Conversational Pathways' feature is explicitly a node-based flow builder where founders give instructions at specific conversation points, test individual node interactions in a pathway editor/testbed, and publish drafts separately from production—matching branching/states/handoffs without code (bland-docs-18, 26, 30, 28, 19). Missing for 10: explicit confirmation of a drag-and-drop visual canvas UI (docs describe 'nodes' and 'pathway editor' but no screenshot/UI walkthrough) and independent hands-on corroboration beyond vendor docs.

   • [claimed-docs] “Conversational pathways are our new way of prompting Bland that has led to major breakthroughs in realism. Give your agent instructions on h…”
   • [claimed-docs] “Conversational pathways are our new way of prompting Bland that has led to major breakthroughs in realism.”
   • [claimed-docs] “Give your agent instructions on how it should respond at specific points of the conversation. Choose between prompting or fixed sentences. E…”
   • [claimed-docs] “The testbed lets you take any call — whether it's a historical call from call logs or a test chat from the pathway editor — isolate a specif…”
   • [claimed-docs] “When you edit a pathway, you are working on a draft. Live calls are not affected. They keep using the published production version.”
   • [claimed-docs] “See the Pathways API reference to create and manage pathways programmatically.”

   proof ↗⚑ flag

Personalization

1. developerInject dynamic variables and per-caller context at call time so each conversation is personalized

   weight 2 · round to ElevenLabs Agents
   ElevenLabs Agents✓fullclaimed8/10

   Docs explicitly state 'Use dynamic variables and overrides for per-conversation customization' as a core build feature, directly matching the story of injecting per-caller context at call time. Missing for 10: no independent/hands-on example showing a real dynamic-variable injection call in code, and no detail on variable scoping/limits beyond the single doc mention.

   • [claimed-docs] “Use dynamic variables and overrides for per-conversation customization”

   proof ↗⚑ flag

   Bland~partialclaimed5/10

   Evidence shows personalization mechanisms exist—Memory for per-caller context (bland-docs-9), batch calls that likely carry per-recipient data (bland-docs-6), and pathway webhooks/API integrations that could fetch live data (bland-docs-4, bland-docs-30)—but there is no explicit documentation of a 'dynamic variables' injection API or call-time variable substitution mechanism. missing for 10: explicit dynamic-variable/request_data injection documentation, examples of per-call variable interpolation into prompts, independent confirmation of personalization working in practice.

   • [claimed-docs] “Memory lets Bland agents remember people across conversations, including calls, SMS, and other channels, so each interaction feels continuou…”
   • [claimed-docs] “Batch calls let you initiate high-volume call campaigns by uploading a list of recipients via CSV.”
   • [claimed-docs] “The API integration lets you connect your AI agent to any HTTP endpoint.”
   • [claimed-docs] “Give your agent instructions on how it should respond at specific points of the conversation. Choose between prompting or fixed sentences. E…”

   proof ↗⚑ flag

2. developerGround the agent on my documents with a built-in knowledge base or RAG so it answers from my content

   weight 2 · round to ElevenLabs Agents
   ElevenLabs Agents✓fullclaimed8/10

   Docs explicitly state agents support uploading documents and enabling RAG for grounded responses, directly matching the story's built-in knowledge base/RAG capability. Missing for 10: independent/hands-on corroboration of RAG quality and details on knowledge base management (chunking, size limits, sources).

   • [claimed-docs] “Upload documents and enable RAG for grounded responses”

   proof ↗⚑ flag

   Bland—none0/10

   Bland is a voice AI/phone agent platform; evidence covers pathways, memory, tools, MCP, CLI, etc., but nowhere mentions a document knowledge base or RAG capability for grounding agent responses in uploaded content. Since a voice/conversational agent platform could plausibly ship this (many competitors do), absence of evidence makes this 'none' rather than 'na'.

   ⚑ flag

Agent access

1. ai-native userPoint an agent at llms.txt or agent-oriented docs

   weight 2 · round to ElevenLabs Agents
   ElevenLabs Agents✓fullprobed9/10

   ElevenLabs publishes a working llms.txt at the root (HTTP 200) and structures its docs as machine-readable .md pages with an explicit pointer to a full docs/llms.txt index, exactly matching the ai-native 'point an agent at llms.txt' pattern; this was independently verified via live probes rather than just claimed docs. Missing for 10: no evidence of a hands-on agent actually consuming the file end-to-end to complete a task (only that the endpoint/format is live and documented).

   • [probe] “PROBE llms.txt: HTTP 200 at https://elevenlabs.io/llms.txt # ElevenLabs ElevenLabs is an AI research and product company transforming how w…”
   • [probe] “PROBE docs-md: HTTP 200 at https://elevenlabs.io/docs/eleven-agents/overview.md > This is a page from the ElevenLabs documentation. For a co…”

   proof ↗⚑ flag

   Bland✓fullprobed8/10

   Bland has a confirmed live llms.txt at docs.bland.ai/llms.txt (HTTP 200) providing agent-oriented documentation, plus an official MCP server and CLI explicitly designed for AI coding agents to interact with the platform via natural language. missing for 10: independent third-party confirmation that agents actually consume llms.txt successfully in practice, and broader agent-oriented docs beyond the single llms.txt file.

   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.bland.ai/llms.txt # Bland Documentation Bland is an enterprise voice AI platform for high-volume, …”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [probe] “official MCP server documented at https://docs.bland.ai/integrations/mcp/overview”
   • [probe] “PROBE runtime (recorded 2026-09-05): keyless JSON-RPC initialize POST to the hosted MCP endpoint https://api.bland.ai/v1/mcp returned HTTP 4…”

   proof ↗⚑ flag

2. ai-native userRun the product headlessly / in CI for automation

   weight 2 · round to Bland
   ElevenLabs Agents~partialprobed6/10

   ElevenLabs Agents can be managed via a full REST API and an official CLI (verified runtime install/run), enabling scripted/headless creation and configuration of agents outside the dashboard, which supports CI-style automation. However, evidence is docs/probe-based with no CI pipeline examples, no first-class 'run in CI' guidance, and Agent testing is described as pre-deploy validation without confirmation it can be triggered non-interactively/headlessly. Missing for 10: explicit CI/CD integration examples, headless testing invocation docs, and independent hands-on confirmation of automated pipelines.

   • [claimed-docs] “ElevenLabs Agents are managed either through the ElevenAgents dashboard, the ElevenLabs API, the Agents CLI or the hosted MCP server.”
   • [claimed-docs] “we'll start by creating an agent via the API or the web dashboard”
   • [claimed-docs] “Agent testing lets you verify conversational responses, tool usage, and full multi-turn outcomes before you deploy.”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y @elevenlabs/cli --version` printed `elevenlabs 1.1.0` keylessly — the official ElevenLabs CLI (…”

   proof ↗⚑ flag

   Bland✓fullprobed8/10

   Bland offers a full programmatic/REST API for pathways, calls, batch calls, webhooks, and evals, plus an official CLI (verified runtime installable keylessly via npx) for terminal-based automation, enabling headless/CI usage. missing for 10: no explicit CI/CD pipeline example (e.g., GitHub Actions) or independent case study confirming CI usage beyond docs and CLI probe.

   • [claimed-docs] “See the Pathways API reference to create and manage pathways programmatically.”
   • [claimed-docs] “Batch calls let you initiate high-volume call campaigns by uploading a list of recipients via CSV.”
   • [claimed-docs] “Manage your entire Bland account from the terminal: make calls, build and test pathways, configure phone numbers, and more.”
   • [probe] “official CLI documented at https://docs.bland.ai/sdks/cli”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y bland-cli --version` printed `0.6.2` keylessly — the official npm CLI (bland-cli, with dev term…”
   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges.”

   proof ↗⚑ flag

3. ai-native userPlug MCP servers into this product so it can use their tools

   weight 3 · round to ElevenLabs Agents
   ElevenLabs Agents✓fullclaimed8/10

   Docs explicitly describe connecting conversational agents to external MCP servers so agents can access data sources and tools via MCP, directly matching the story, and this is corroborated by dedicated documentation pages (mcp.md) rather than just passing mention. Missing for 10: independent/hands-on verification of an actual third-party MCP server being plugged in and tool-calls succeeding in production (only docs and a runtime probe on the separate hosted-server feature are present).

   • [claimed-docs] “ElevenLabs allows you to connect your conversational agents to external MCP servers. This enables your agents to: Access and process informa…”
   • [claimed-docs] “ElevenLabs allows you to connect your conversational agents to external MCP servers.”
   • [claimed-docs] “Access and process information from various data sources via the MCP server”

   proof ↗⚑ flag

   Bland—none0/10

   Bland's evidence only documents it exposing an outbound MCP server so that external AI coding agents can call Bland's own tools (docs-11, docs-20, docs-27, probe-rt-2) — the reverse direction of this story. There is no evidence that Bland itself can consume/plug in third-party MCP servers as a client; its tool integration story is limited to custom HTTP API endpoints and webhooks (bland-docs-4, bland-docs-25).

   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The API integration lets you connect your AI agent to any HTTP endpoint.”
   • [claimed-docs] “Connect external APIs and take live actions during phone calls.”
   • [probe] “PROBE runtime (recorded 2026-09-05): keyless JSON-RPC initialize POST to the hosted MCP endpoint https://api.bland.ai/v1/mcp returned HTTP 4…”

   proof ↗⚑ flag

4. ai-native userConnect an agent via an official MCP server

   weight 3 · round to ElevenLabs Agents
   ElevenLabs Agents✓fullprobed9/10

   ElevenLabs explicitly documents and operates a hosted MCP server (api.elevenlabs.io/v1/mcp) that lets external MCP clients like Claude connect to create, configure, and manage agents through natural language, with a runtime probe confirming the live endpoint correctly responds with the MCP OAuth challenge. This is a first-party 'official MCP server' offering, not just client-side MCP consumption, matching the story precisely. Missing for 10: independent/community hands-on confirmation beyond ElevenLabs' own docs and the single runtime probe.

   • [claimed-docs] “ElevenLabs Agents are managed either through the ElevenAgents dashboard, the ElevenLabs API, the Agents CLI or the hosted MCP server.”
   • [claimed-docs] “an assistant such as Claude can create, configure, and manage the agents in your workspace through natural language, with nothing to install…”
   • [claimed-docs] “connect the hosted MCP server to Claude or any other MCP client to create and manage agents through natural language”
   • [claimed-docs] “create, configure, and manage the agents in your workspace through natural language, with nothing to install or run locally”
   • [probe] “official MCP server documented at https://elevenlabs.io/docs/eleven-agents/operate/hosted-mcp”
   • [probe] “PROBE runtime (recorded 2026-09-05): keyless JSON-RPC initialize POST to the hosted MCP server https://api.elevenlabs.io/v1/mcp returned HTT…”

   proof ↗⚑ flag

   Bland✓fullprobed8/10

   Bland ships an official MCP server (docs and runtime probe confirm it's live and gated by API key) that lets AI coding agents place/inspect calls, build pathways, manage agents, query analytics, run evals, and search docs — directly fulfilling the story. Missing for 10: independent third-party hands-on review of the MCP server beyond vendor docs/probe.

   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [probe] “official MCP server documented at https://docs.bland.ai/integrations/mcp/overview”
   • [probe] “PROBE runtime (recorded 2026-09-05): keyless JSON-RPC initialize POST to the hosted MCP endpoint https://api.bland.ai/v1/mcp returned HTTP 4…”

   proof ↗⚑ flag

5. ai-native userUse an official CLI

   weight 2 · round drawn
   ElevenLabs Agents✓fullprobed9/10

   ElevenLabs documents an official Agents CLI for managing agents, and runtime probing confirms `npx @elevenlabs/cli` installs and runs keylessly from npm, corroborating the docs claim with hands-on evidence. Missing for 10: deeper documentation of full CLI command surface/subcommands beyond basic install confirmation.

   • [claimed-docs] “ElevenLabs Agents are managed either through the ElevenAgents dashboard, the ElevenLabs API, the Agents CLI or the hosted MCP server.”
   • [claimed-docs] “Use the ElevenLabs agents skill to build and manage voice agents from your AI coding assistant: npx skills add elevenlabs/skills --skill age…”
   • [claimed-docs] “Use the ElevenLabs agents skill to build and manage voice agents from your AI coding assistant”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y @elevenlabs/cli --version` printed `elevenlabs 1.1.0` keylessly — the official ElevenLabs CLI (…”

   proof ↗⚑ flag

   Bland✓fullprobed9/10

   Bland ships an official CLI (bland-cli) documented to manage the entire account from the terminal, and a runtime probe confirms it installs and runs via npx keylessly. missing for 10: independent third-party review/usage reports of the CLI beyond the official docs and one probe run.

   • [claimed-docs] “Manage your entire Bland account from the terminal: make calls, build and test pathways, configure phone numbers, and more.”
   • [probe] “official CLI documented at https://docs.bland.ai/sdks/cli”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y bland-cli --version` printed `0.6.2` keylessly — the official npm CLI (bland-cli, with dev term…”

   proof ↗⚑ flag

6. ai-native userDrive the product through a documented public API

   weight 3 · round drawn
   ElevenLabs Agents✓fullprobed9/10

   ElevenLabs Agents ships a documented public API alongside a dashboard, CLI, and hosted MCP server for programmatic agent creation/management, with quickstart docs explicitly noting 'creating an agent via the API' and full customization (tools, voice, analysis, testing) accessible programmatically. Runtime probes confirm the CLI installs keylessly and the hosted MCP endpoint is live and functional. Missing for 10: no independent third-party API reference/SDK usage examples or community corroboration beyond first-party docs.

   • [claimed-docs] “ElevenLabs Agents are managed either through the ElevenAgents dashboard, the ElevenLabs API, the Agents CLI or the hosted MCP server.”
   • [claimed-docs] “we'll start by creating an agent via the API or the web dashboard”
   • [claimed-docs] “Enable agents to call clients & APIs to perform actions”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y @elevenlabs/cli --version` printed `elevenlabs 1.1.0` keylessly — the official ElevenLabs CLI (…”
   • [probe] “PROBE runtime (recorded 2026-09-05): keyless JSON-RPC initialize POST to the hosted MCP server https://api.elevenlabs.io/v1/mcp returned HTT…”

   proof ↗⚑ flag

   Bland✓fullprobed9/10

   Bland documents a comprehensive public API (pathways, calls, tools, webhooks, evals, batch calls) plus SDKs, CLI, and an official MCP server, and runtime probes confirm the CLI installs and the hosted MCP endpoint is live and gated as documented, showing agentic programmatic control. Missing for 10: a formally published OpenAPI/Swagger spec (probe found 404s at standard OpenAPI paths), so machine-readable spec discoverability is unconfirmed.

   • [claimed-docs] “See the Pathways API reference to create and manage pathways programmatically.”
   • [claimed-docs] “The API integration lets you connect your AI agent to any HTTP endpoint.”
   • [claimed-docs] “Batch calls let you initiate high-volume call campaigns by uploading a list of recipients via CSV.”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “Manage your entire Bland account from the terminal: make calls, build and test pathways, configure phone numbers, and more.”
   • [probe] “official MCP server documented at https://docs.bland.ai/integrations/mcp/overview”
   • [probe] “official CLI documented at https://docs.bland.ai/sdks/cli”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y bland-cli --version` printed `0.6.2` keylessly — the official npm CLI (bland-cli, with dev term…”
   • [probe] “PROBE runtime (recorded 2026-09-05): keyless JSON-RPC initialize POST to the hosted MCP endpoint https://api.bland.ai/v1/mcp returned HTTP 4…”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.bland.ai/openapi.json, https://docs.bland.ai/swagger.json, https://docs.bland.ai/api/op…”

   proof ↗⚑ flag

7. ai-native userIssue scoped/least-privilege API credentials for an agent

   weight 2 · round drawn
   ElevenLabs Agents—none0/10

   Evidence covers agent-side authentication (protecting inbound access to a deployed agent) and OAuth for the hosted MCP server, but there is no documentation of issuing scoped or least-privilege API keys/tokens that limit what an agent itself can do via the ElevenLabs API. Missing for 10: any docs on API key permission scopes, workspace role-based tokens, or restricted-credential issuance for agents.

   • [claimed-docs] “Secure agent access | Authentication | Implement custom authentication for protected agent access”
   • [claimed-docs] “Implement custom authentication for protected agent access”
   • [probe] “PROBE runtime (recorded 2026-09-05): keyless JSON-RPC initialize POST to the hosted MCP server https://api.elevenlabs.io/v1/mcp returned HTT…”

   proof ↗⚑ flag

   Bland—none0/10

   Evidence shows generic API-key auth and JWT-based webhook verification (bland-docs-15), but nothing about issuing scoped or least-privilege credentials specific to an agent's permissions (e.g., role-based API keys, scoped tokens limiting call/pathway/account access). Missing for 10: documentation of scoped API key creation, permission levels, or per-agent credential restriction.

   • [claimed-docs] “JWT signatures eliminate these risks through asymmetric cryptography - you verify requests using our public JWKS endpoint without storing an…”

   proof ↗⚑ flag

8. ai-native userBuild against official SDKs

   weight 2 · round to Bland
   ElevenLabs Agents~partialprobed4/10

   Docs confirm programmatic access via the ElevenLabs API and an official CLI (`@elevenlabs/cli`, verified runtime) for building agents, but no evidence pack item explicitly documents a first-party language SDK (Python/Node/etc.) for Agents beyond the generic API reference. Missing for 10: explicit official SDK docs/repos, SDK code examples, and independent confirmation of SDK feature parity with the API.

   • [claimed-docs] “ElevenLabs Agents are managed either through the ElevenAgents dashboard, the ElevenLabs API, the Agents CLI or the hosted MCP server.”
   • [claimed-docs] “we'll start by creating an agent via the API or the web dashboard”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y @elevenlabs/cli --version` printed `elevenlabs 1.1.0` keylessly — the official ElevenLabs CLI (…”

   proof ↗⚑ flag

   Bland~partialprobed6/10

   Bland documents a Web Agent SDK for embedding voice agents (React/Vanilla JS/Node), a CLI, and a REST API used throughout tutorials, giving AI-native developers concrete building blocks; runtime probes confirm the CLI installs and runs. However, no dedicated 'official SDK' page for server-side languages (Python/Node backend SDK) is evidenced, and openapi/swagger spec endpoints all 404, suggesting the API reference isn't machine-consumable in a standard SDK-generation format. Missing for 10: a clearly documented multi-language backend SDK (Python/Node) beyond the browser widget SDK, and a working OpenAPI spec for auto-generating clients.

   • [claimed-docs] “Embed a Bland voice agent into any web application (React, Vanilla JS, or Node). The SDK handles secure authentication between your server a…”
   • [claimed-docs] “Manage your entire Bland account from the terminal: make calls, build and test pathways, configure phone numbers, and more.”
   • [probe] “official CLI documented at https://docs.bland.ai/sdks/cli”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y bland-cli --version` printed `0.6.2` keylessly — the official npm CLI (bland-cli, with dev term…”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.bland.ai/openapi.json, https://docs.bland.ai/swagger.json, https://docs.bland.ai/api/op…”
   • [claimed-docs] “See the Pathways API reference to create and manage pathways programmatically.”

   proof ↗⚑ flag

9. ai-native userSubscribe to events via webhooks

   weight 2 · round to Bland
   ElevenLabs Agents—none0/10

   The evidence describes 'webhook tools' that let an agent make outbound calls to external endpoints during a conversation (docs-14, docs-29, docs-31), which is the opposite of subscribing to platform-emitted events via webhooks. There is no documented mechanism for a developer to register a webhook URL to receive event notifications (e.g., conversation-ended, call-completed) from ElevenLabs Agents.

   • [claimed-docs] “Tools give your assistant the ability to make external function calls to third party apps so you can get real-time information.”
   • [claimed-docs] “Tools allow ElevenLabs agents to perform actions beyond generating text responses.”
   • [claimed-docs] “allow an assistant to trigger authenticated actions based on the conversation, like scheduling meetings or initiating order returns”

   proof ↗⚑ flag

   Bland~partialclaimed6/10

   Bland documents post-call webhooks (automatic HTTP notifications sent after a call completes) and pathway-level webhook execution at specific conversation points, showing genuine event-driven webhook support tied to call lifecycle. However, evidence only covers call-related events (completion, in-call triggers) — missing for 10: a general-purpose event subscription/webhook management API covering other account events (e.g., evals, batch campaign status, pathway publishes), and any independent confirmation of webhook reliability/configuration options.

   • [claimed-docs] “Post-call webhooks are HTTP notifications that Bland AI automatically sends to your server after a phone call completes.”
   • [claimed-docs] “Give your agent instructions on how it should respond at specific points of the conversation. Choose between prompting or fixed sentences. E…”

   proof ↗⚑ flag

Agentic features

1. ai-native userGet AI-generated insights and suggestions from my data inside the product

   weight 2 · round to ElevenLabs Agents
   ElevenLabs Agents~partialclaimed5/10

   Agent-analysis features (sentiment analysis, success evaluation, structured data extraction, semantic conversation search) generate AI-derived insights from the user's own conversation data, which partially satisfies the story. However, these are evaluation/reporting metrics rather than proactive AI-generated suggestions or recommendations for next actions, and there is no independent/hands-on corroboration of this analytics experience. Missing for 10: evidence of proactive suggestion generation (e.g., recommended actions/optimizations), a dashboard summarization view, and independent verification beyond first-party docs.

   • [claimed-docs] “Success evaluation: Define custom criteria to assess conversation quality, goal achievement, and customer satisfaction”
   • [claimed-docs] “Data collection: Extract specific data points from conversations such as contact information, issue details, or any structured information”
   • [claimed-docs] “Sentiment analysis: Understand user sentiment across completed conversations”
   • [claimed-docs] “Searching conversations: Find messages by keyword or meaning across your conversation history.”
   • [claimed-docs] “Define custom criteria to assess conversation quality, goal achievement, and customer satisfaction”
   • [claimed-docs] “Extract structured information from conversations such as contact details and business data”

   proof ↗⚑ flag

   Bland~partialclaimed4/10

   Bland offers LLM-judge Evals to grade call quality and an MCP integration that can 'query analytics' on your account data, which are AI-generated evaluative outputs derived from your call data, but there's no dedicated insights/suggestions dashboard or proactive recommendation feature described. Missing for 10: a native analytics/insights UI, evidence of proactive suggestions surfaced to users, and independent confirmation of these AI-generated insights in practice.

   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges.”
   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges. You define eval agents, each of which grades one dimension of a call”
   • [claimed-docs] “Memory lets Bland agents remember people across conversations, including calls, SMS, and other channels, so each interaction feels continuou…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”

   proof ↗⚑ flag

2. ai-native userSet up automations that run autonomously in the background

   weight 2 · round to Bland
   ElevenLabs Agents~partialclaimed4/10

   ElevenLabs Agents supports tool-calling, webhook actions, and multi-step workflows that let a voice agent perform actions (scheduling, API calls, MCP access) during a conversation, and agents can be created/managed via API for programmatic triggering, which give some automation flavor. But all documented action-taking is tied to a live conversational turn (voice/chat) rather than an autonomous background process that runs without a triggering conversation or human interaction, so the 'runs autonomously in the background' framing is only weakly supported. Missing for 10: evidence of scheduled/cron-triggered autonomous runs, evidence of multi-step tasks executing without a live conversation session, and independent confirmation of background automation outside the demoed conversational/tool-call flow.

   • [claimed-docs] “Build multi-step workflows with visual workflow builder”
   • [claimed-docs] “Enable agents to call clients & APIs to perform actions”
   • [claimed-docs] “Tools give your assistant the ability to make external function calls to third party apps so you can get real-time information.”
   • [claimed-docs] “allow an assistant to trigger authenticated actions based on the conversation, like scheduling meetings or initiating order returns”
   • [claimed-docs] “ElevenLabs allows you to connect your conversational agents to external MCP servers. This enables your agents to: Access and process informa…”
   • [claimed-docs] “we'll start by creating an agent via the API or the web dashboard”

   proof ↗⚑ flag

   Bland~partialclaimed6/10

   Bland supports batch calls, webhooks, pathways, and scheduled/triggered call campaigns that run without manual intervention, which constitute a form of autonomous background automation for voice workflows. However, this is scoped to phone-call automation only, not general-purpose background task/agent scheduling. missing for 10: evidence of a generic scheduler/cron-like trigger system, independent hands-on validation of unattended background runs, and confirmation of failure handling/monitoring for long-running autonomous automations.

   • [claimed-docs] “Batch calls let you initiate high-volume call campaigns by uploading a list of recipients via CSV.”
   • [claimed-docs] “Post-call webhooks are HTTP notifications that Bland AI automatically sends to your server after a phone call completes.”
   • [claimed-docs] “Conversational pathways are our new way of prompting Bland that has led to major breakthroughs in realism. Give your agent instructions on h…”
   • [claimed-docs] “Give your agent instructions on how it should respond at specific points of the conversation. Choose between prompting or fixed sentences. E…”
   • [claimed-docs] “Dispatch AI phone calls to call customers, leads, and to streamline operations.”
   • [claimed-docs] “Connect external APIs and take live actions during phone calls.”

   proof ↗⚑ flag

3. ai-native userDelegate tasks to a built-in AI assistant inside the product

   weight 3 · round to Bland
   ElevenLabs Agents~partialprobed5/10

   ElevenLabs documents a first-party hosted MCP server letting an assistant like Claude create, configure, and manage agents in the workspace via natural language, with nothing to install locally — a form of task delegation to an AI assistant for managing the product. However this is not a native in-product chat/assistant UI; it requires an external MCP client (e.g., Claude) to act as the assistant, and delegation is scoped to agent-management tasks rather than general product tasks. Missing for 10: a built-in assistant surface inside the ElevenLabs dashboard itself, evidence of broader task delegation beyond agent CRUD, and independent hands-on confirmation of the natural-language workflow.

   • [claimed-docs] “an assistant such as Claude can create, configure, and manage the agents in your workspace through natural language, with nothing to install…”
   • [claimed-docs] “an assistant such as Claude can create, configure, and manage the agents in your workspace through natural language”
   • [claimed-docs] “connect the hosted MCP server to Claude or any other MCP client to create and manage agents through natural language”
   • [claimed-docs] “create, configure, and manage the agents in your workspace through natural language, with nothing to install or run locally”
   • [probe] “PROBE runtime (recorded 2026-09-05): keyless JSON-RPC initialize POST to the hosted MCP server https://api.elevenlabs.io/v1/mcp returned HTT…”

   proof ↗⚑ flag

   Bland✓fullclaimed8/10

   Bland's core product is a built-in AI voice agent that users delegate tasks to (placing/answering calls, executing pathways, calling APIs, remembering context) rather than a separate feature bolted on — e.g., 'Dispatch AI phone calls to call customers, leads, and to streamline operations' and pathway/tool/memory docs show rich task delegation to the built-in agent. Missing for 10: independent/hands-on evidence of real-world task delegation outcomes beyond vendor docs.

   • [claimed-docs] “Dispatch AI phone calls to call customers, leads, and to streamline operations.”
   • [claimed-docs] “Conversational pathways are our new way of prompting Bland that has led to major breakthroughs in realism. Give your agent instructions on h…”
   • [claimed-docs] “Connect external APIs and take live actions during phone calls.”
   • [claimed-docs] “Memory lets Bland agents remember people across conversations, including calls, SMS, and other channels, so each interaction feels continuou…”
   • [claimed-docs] “Give your agent instructions on how it should respond at specific points of the conversation. Choose between prompting or fixed sentences. E…”

   proof ↗⚑ flag

4. ai-native userOperate the product with natural-language commands

   weight 2 · round drawn
   ElevenLabs Agents✓fullprobed8/10

   ElevenLabs Agents ships a hosted MCP server explicitly designed so assistants like Claude can 'create, configure, and manage the agents in your workspace through natural language, with nothing to install or run locally,' and a runtime probe confirms the endpoint is live and speaks the MCP OAuth flow. This is a direct, first-party natural-language control surface, not just a client-side integration. Missing for 10: independent/community hands-on confirmation of a full natural-language agent-management session (only endpoint liveness was probed, not an actual successful NL task).

   • [claimed-docs] “an assistant such as Claude can create, configure, and manage the agents in your workspace through natural language, with nothing to install…”
   • [claimed-docs] “an assistant such as Claude can create, configure, and manage the agents in your workspace through natural language”
   • [claimed-docs] “connect the hosted MCP server to Claude or any other MCP client to create and manage agents through natural language”
   • [claimed-docs] “create, configure, and manage the agents in your workspace through natural language, with nothing to install or run locally”
   • [probe] “PROBE runtime (recorded 2026-09-05): keyless JSON-RPC initialize POST to the hosted MCP server https://api.elevenlabs.io/v1/mcp returned HTT…”
   • [probe] “official MCP server documented at https://elevenlabs.io/docs/eleven-agents/operate/hosted-mcp”

   proof ↗⚑ flag

   Bland✓fullprobed8/10

   Bland offers an official MCP server that lets AI agents operate the entire account (calls, pathways, agents, analytics, evals) via natural language, plus an official CLI, both confirmed live via runtime probes. missing for 10: independent/hands-on third-party review of the MCP/CLI natural-language experience beyond vendor docs and probes.

   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [probe] “official MCP server documented at https://docs.bland.ai/integrations/mcp/overview”
   • [probe] “PROBE runtime (recorded 2026-09-05): keyless JSON-RPC initialize POST to the hosted MCP endpoint https://api.bland.ai/v1/mcp returned HTTP 4…”
   • [claimed-docs] “Manage your entire Bland account from the terminal: make calls, build and test pathways, configure phone numbers, and more.”
   • [probe] “official CLI documented at https://docs.bland.ai/sdks/cli”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y bland-cli --version` printed `0.6.2` keylessly — the official npm CLI (bland-cli, with dev term…”

   proof ↗⚑ flag

Api quality

1. ai-native userExplore an interactive API reference with runnable examples

   weight 2 · round drawn
   ElevenLabs Agents—none0/10

   The evidence pack contains extensive markdown documentation for ElevenLabs Agents (quickstart, customization, tools, etc.) but nothing describes an interactive API reference page with runnable/'try it' examples — no mention of a Swagger/OpenAPI explorer, live code sandbox, or embedded runnable snippets. Since ElevenLabs Agents exposes an API, this axis is applicable, but no evidence supports the specific capability.

   ⚑ flag

   Bland—none0/10

   Docs reference an API reference for pathways but no evidence of an interactive playground with runnable examples; a probe for OpenAPI/Swagger specs at standard paths returned 404s, suggesting no such interactive reference exists.

   • [claimed-docs] “See the Pathways API reference to create and manage pathways programmatically.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.bland.ai/openapi.json, https://docs.bland.ai/swagger.json, https://docs.bland.ai/api/op…”

   proof ↗⚑ flag

2. ai-native userDownload a machine-readable API spec (OpenAPI or equivalent)

   weight 2 · round drawn
   ElevenLabs Agents—none0/10

   The evidence pack documents the API, CLI, dashboard, and hosted MCP server for ElevenLabs Agents, but nowhere mentions a downloadable OpenAPI/Swagger spec or machine-readable schema for the API. This axis clearly applies to an API-driven product, but no evidence shows it being delivered.

   ⚑ flag

   Bland—none0/10

   A direct probe found no OpenAPI/Swagger spec at any expected location (all 404s), and no evidence pack item shows a downloadable machine-readable API spec despite extensive API documentation.

   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.bland.ai/openapi.json, https://docs.bland.ai/swagger.json, https://docs.bland.ai/api/op…”

   proof ↗⚑ flag

3. ai-native userTest against a sandbox environment without touching production data

   weight 1 · round to Bland
   ElevenLabs Agents~partialclaimed4/10

   Docs describe an 'agent testing' feature to verify conversational responses, tool usage, and multi-turn outcomes 'before you deploy', implying a pre-production test workflow, and tool execution can run in a 'sandboxed environment' on ElevenLabs' infrastructure. However there is no explicit documentation of a separate sandbox/staging environment or test data isolation from production conversation/analytics data. Missing for 10: explicit sandbox/staging environment separate from production data, documentation of test-vs-prod data isolation, independent confirmation that test runs don't affect production analytics or logs.

   • [claimed-docs] “Agent testing lets you verify conversational responses, tool usage, and full multi-turn outcomes before you deploy.”
   • [claimed-docs] “Agent testing lets you verify conversational responses, tool usage, and full multi-turn outcomes before you deploy”
   • [claimed-docs] “Custom JavaScript executed in a sandboxed environment on ElevenLabs' infrastructure.”
   • [claimed-docs] “Custom JavaScript executed in a sandboxed environment on ElevenLabs' infrastructure”

   proof ↗⚑ flag

   Bland~partialclaimed6/10

   Bland provides draft/staging pathway editing where live production calls are unaffected, plus a testbed for isolated node testing, canary deployments, and staged version adoption — all functioning as sandbox-like mechanisms distinct from production. However, there's no explicit 'sandbox environment' or dedicated test account/data isolation concept described, and testing still appears to involve real calls/production infrastructure rather than a fully isolated non-production environment. missing for 10: a dedicated sandbox/test-mode account distinct from production billing and phone infrastructure, explicit documentation of synthetic/non-production test data, and independent confirmation that testbed/draft testing never touches real production call data or costs.

   • [claimed-docs] “You can also promote a version to staging to test it before it goes live, or send an individual call against a specific”
   • [claimed-docs] “When you edit a pathway, you are working on a draft. Live calls are not affected. They keep using the published production version.”
   • [claimed-docs] “The testbed lets you take any call ... isolate a specific node interaction, edit the prompt, and run it multiple times to see how the output…”
   • [claimed-docs] “The testbed lets you take any call — whether it's a historical call from call logs or a test chat from the pathway editor — isolate a specif…”
   • [claimed-docs] “Bland supports canary deployments — a way to run a new release on a separate set of containers alongside your production infrastructure and …”
   • [claimed-docs] “This is how you A/B test a new agent release against your live production version, with real calls, before committing to a full rollout.”

   proof ↗⚑ flag

4. ai-native userRely on versioned APIs with a documented deprecation policy

   weight 2 · round to Bland
   ElevenLabs Agents—none0/10

   No evidence in the pack mentions API versioning scheme, version numbers, or any documented deprecation policy for ElevenLabs Agents' API/CLI/MCP interfaces. missing for 10: versioned API endpoints or version headers, changelog/release notes, explicit deprecation policy or sunset timelines, migration guides for breaking changes.

   ⚑ flag

   Bland~partialprobed4/10

   Bland documents infrastructure versioning concepts (staged/canary rollouts, choosing when to adopt a new release, draft vs. production pathway versions) but there is no evidence of a documented API versioning scheme (e.g., v1/v2 endpoints) or an explicit deprecation policy for its APIs, and the OpenAPI spec probe returned 404s. missing for 10: documented API version numbering/endpoints, an explicit deprecation/sunset policy for APIs, published OpenAPI spec, and independent confirmation of versioning practices.

   • [claimed-docs] “You choose when to adopt a new release. Your infrastructure stays on the version you've selected until you decide to move forward.”
   • [claimed-docs] “Bland supports canary deployments — a way to run a new release on a separate set of containers alongside your production infrastructure and …”
   • [claimed-docs] “You choose when to adopt a new release... Bland supports canary deployments — a way to run a new release on a separate set of containers alo…”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.bland.ai/openapi.json, https://docs.bland.ai/swagger.json, https://docs.bland.ai/api/op…”

   proof ↗⚑ flag

1. ai-native userPerform bulk operations across many items at once

   weight 2 · round to Bland
   ElevenLabs Agents—none0/10

   The evidence documents API, CLI, and MCP-based management of agents, but nothing describes bulk/batch operations (e.g., batch-create, batch-update, or batch-delete across many agents or conversations at once). This axis is fair to ask given the product exposes an API and CLI, but no evidence confirms bulk-operation support.

   ⚑ flag

   Bland✓fullprobed7/10

   Batch calls let users upload a CSV of recipients to initiate high-volume call campaigns, directly enabling bulk operations across many items, and the CLI/MCP server extend programmatic/bulk management of pathways, agents, and calls. Missing for 10: independent/hands-on verification of batch call performance at scale, and documentation of bulk operations beyond calls (e.g., bulk pathway or number management).

   • [claimed-docs] “Batch calls let you initiate high-volume call campaigns by uploading a list of recipients via CSV.”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “Manage your entire Bland account from the terminal: make calls, build and test pathways, configure phone numbers, and more.”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y bland-cli --version` printed `0.6.2` keylessly — the official npm CLI (bland-cli, with dev term…”

   proof ↗⚑ flag

2. ai-native userDefine rules that trigger actions automatically on events

   weight 3 · round to Bland
   ElevenLabs Agents~partialclaimed6/10

   ElevenLabs Agents supports tool-calling and webhook actions that are triggered automatically based on conversation events (e.g., 'trigger authenticated actions based on the conversation, like scheduling meetings or initiating order returns'), plus multi-step visual workflows and MCP-based external actions. This covers event-triggered automation within a conversation but the evidence doesn't show general rule-based triggers on arbitrary system events (e.g., scheduled/external triggers outside a live conversation) — missing for 10: documentation of standalone event/rule engine independent of a live conversation, examples of conditional branching logic beyond tool calls, and independent verification of automation reliability.

   • [claimed-docs] “Enable agents to call clients & APIs to perform actions”
   • [claimed-docs] “Tools give your assistant the ability to make external function calls to third party apps so you can get real-time information.”
   • [claimed-docs] “allow an assistant to trigger authenticated actions based on the conversation, like scheduling meetings or initiating order returns”
   • [claimed-docs] “Build multi-step workflows with visual workflow builder”
   • [claimed-docs] “ElevenLabs allows you to connect your conversational agents to external MCP servers. This enables your agents to: Access and process informa…”

   proof ↗⚑ flag

   Bland✓fullclaimed7/10

   Bland's pathways let users define conditional logic that executes webhooks/API calls at specific conversation nodes, and post-call webhooks automatically fire HTTP notifications when a call-completion event occurs — this is a documented rules-trigger-action-on-event mechanism. missing for 10: independent/hands-on verification of the webhook triggering in production, and evidence of event types beyond call-based ones (e.g., generic account-level event automation).

   • [claimed-docs] “Post-call webhooks are HTTP notifications that Bland AI automatically sends to your server after a phone call completes.”
   • [claimed-docs] “Give your agent instructions on how it should respond at specific points of the conversation. Choose between prompting or fixed sentences. E…”
   • [claimed-docs] “Connect external APIs and take live actions during phone calls.”
   • [claimed-docs] “Conversational pathways are our new way of prompting Bland that has led to major breakthroughs in realism. Give your agent instructions on h…”

   proof ↗⚑ flag

3. ai-native userSchedule recurring jobs or workflows

   weight 2 · round drawn
   ElevenLabs Agents—none0/10

   The evidence pack covers multi-step workflow building, tool calling, MCP integration, and analysis, but no documentation mentions scheduling recurring jobs, cron-like triggers, or automated recurring workflow execution. This is a fair capability to expect from an automation-focused agent platform, but no evidence supports it.

   ⚑ flag

   Bland—none0/10

   Bland's docs cover batch calls, pathways, webhooks, and API integrations, but nothing describes scheduling recurring jobs or workflows (e.g., cron-like triggers for calls or campaigns) — batch calls are one-off CSV uploads, not recurring schedules.

   • [claimed-docs] “Batch calls let you initiate high-volume call campaigns by uploading a list of recipients via CSV.”
   • [claimed-docs] “Dispatch AI phone calls to call customers, leads, and to streamline operations.”

   proof ↗⚑ flag

4. ai-native userVersion, review, and roll back my automations

   weight 1 · round to Bland
   ElevenLabs Agents—none0/10

   The docs describe building, testing, and analyzing agents (agent-testing, agent-analysis) but there is no mention of version history, change review workflows, or rollback/revert capability for agent configurations. Missing for 10: version history UI, rollback/restore of prior agent configs, change-review/approval workflow, audit trail of edits.

   ⚑ flag

   Bland~partialclaimed6/10

   Bland pathways support draft/staging/production versioning, A/B testing new releases against live production, and canary/staged rollout with adoption control (bland-docs-2, bland-docs-14, bland-docs-19, bland-docs-21, bland-docs-29, bland-docs-31), plus a testbed and evals for reviewing behavior before shipping (bland-docs-7, bland-docs-8, bland-docs-22, bland-docs-28). However, there is no explicit documentation of an automated 'rollback' mechanism to revert a live pathway/release to a prior version — only forward-adoption and canary controls are described. Missing for 10: explicit rollback/revert-to-previous-version capability, version history/diff view, and independent confirmation of rollback in practice.

   • [claimed-docs] “You can also promote a version to staging to test it before it goes live, or send an individual call against a specific”
   • [claimed-docs] “You choose when to adopt a new release. Your infrastructure stays on the version you've selected until you decide to move forward.”
   • [claimed-docs] “When you edit a pathway, you are working on a draft. Live calls are not affected. They keep using the published production version.”
   • [claimed-docs] “This is how you A/B test a new agent release against your live production version, with real calls, before committing to a full rollout.”
   • [claimed-docs] “Bland supports canary deployments — a way to run a new release on a separate set of containers alongside your production infrastructure and …”
   • [claimed-docs] “You choose when to adopt a new release... Bland supports canary deployments — a way to run a new release on a separate set of containers alo…”
   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges.”
   • [claimed-docs] “The testbed lets you take any call ... isolate a specific node interaction, edit the prompt, and run it multiple times to see how the output…”
   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges. You define eval agents, each of which grades one dimension of a call”
   • [claimed-docs] “The testbed lets you take any call — whether it's a historical call from call logs or a test chat from the pathway editor — isolate a specif…”

   proof ↗⚑ flag

Compliance

1. founderMeet call-recording consent and disclosure obligations with per-call recording controls and configurable data retention

   weight 2 · round to ElevenLabs Agents
   ElevenLabs Agents~partialclaimed4/10

   Docs confirm configurable data-retention settings for conversation transcripts and audio recordings, which addresses the retention half of the story, plus HIPAA/BAA support suggesting compliance posture. However, there is no evidence of per-call recording enable/disable controls or built-in consent/disclosure messaging mechanisms for calls. Missing for 10: per-call recording toggle, consent/disclosure prompt configuration, and any independent verification of retention enforcement.

   • [claimed-docs] “Retention settings allow you to configure how long your conversational agent stores conversation transcripts and audio recordings.”
   • [claimed-docs] “Retention settings allow you to configure how long your conversational agent stores conversation transcripts and audio recordings”
   • [claimed-docs] “ElevenLabs allows such customers to develop AI-powered voice agents for the handling Protected Health Information (PHI)”
   • [claimed-docs] “ElevenLabs Agents is one of ElevenLabs' HIPAA-eligible services, and we offer Business Associate Agreements (BAAs) to eligible customers”

   proof ↗⚑ flag

   Bland—none0/10

   The evidence pack contains no mention of call recording consent/disclosure features, per-call recording toggles, or configurable data retention policies anywhere in Bland's docs; while this is a fair compliance axis for a voice AI platform, nothing in the pack substantiates it.

   ⚑ flag

2. platform-engineerRun regulated workloads with HIPAA/BAA support, SOC 2, and data-residency options

   weight 2 · round to ElevenLabs Agents
   ElevenLabs Agents~partialclaimed5/10

   ElevenLabs Agents documents HIPAA eligibility and BAA availability for eligible customers, directly supporting regulated PHI workloads. However, there is no evidence in the pack of SOC 2 certification or data-residency options, both explicitly named in the story. Missing for 10: SOC 2 attestation/report evidence, data-residency region selection or documentation, independent audit corroboration of HIPAA/BAA claims.

   • [claimed-docs] “ElevenLabs allows such customers to develop AI-powered voice agents for the handling Protected Health Information (PHI)”
   • [claimed-docs] “ElevenLabs allows such customers to develop AI-powered voice agents for the handling Protected Health Information (PHI).”
   • [claimed-docs] “ElevenLabs Agents is one of ElevenLabs' HIPAA-eligible services, and we offer Business Associate Agreements (BAAs) to eligible customers”

   proof ↗⚑ flag

   Bland—none0/10

   The evidence pack contains no mention of HIPAA, BAA, SOC 2 certification, or data-residency options anywhere in the docs or probes; only a generic tagline calling Bland an 'enterprise' platform for 'regulated' workflows without specifics. Missing for 10: HIPAA/BAA documentation, SOC 2 report or certification evidence, data-residency/region controls, and any compliance attestations.

   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.bland.ai/llms.txt # Bland Documentation Bland is an enterprise voice AI platform for high-volume, …”

   proof ↗⚑ flag

Scale

1. platform-engineerSee documented concurrency limits and scale to many simultaneous calls without manual capacity begging

   weight 2 · round to Bland
   ElevenLabs Agents—none0/10

   No evidence pack item documents concurrency limits, per-account call caps, autoscaling behavior, or a process for scaling to many simultaneous calls without contacting sales/support; docs cover features like tools, voice customization, SIP trunking, HIPAA, and MCP but nothing on capacity/scale numbers or self-service scaling guarantees.

   ⚑ flag

   Bland~partialclaimed3/10

   Docs mention batch calling for high-volume campaigns and enterprise infrastructure controls (canary releases, staged rollout), implying some capacity for scale, but there is no documented concurrency limit, rate ceiling, or auto-scaling guarantee, and no evidence that capacity increases don't require contacting sales/support. Missing for 10: explicit concurrent-call limits, auto-scaling documentation, and evidence that scaling doesn't require manual requests to Bland.

   • [claimed-docs] “Batch calls let you initiate high-volume call campaigns by uploading a list of recipients via CSV.”
   • [claimed-docs] “Bland supports canary deployments — a way to run a new release on a separate set of containers alongside your production infrastructure and …”
   • [claimed-docs] “You choose when to adopt a new release... Bland supports canary deployments — a way to run a new release on a separate set of containers alo…”

   proof ↗⚑ flag

Self host

1. platform-engineerSelf-host the voice agent runtime from open-source code on my own infrastructure

   weight 3 · round drawn
   ElevenLabs Agents—none0/10

   ElevenLabs Agents is entirely a managed/hosted service — the CLI and MCP server are clients/interfaces to ElevenLabs' cloud infrastructure, not open-source runtime code that can be deployed on a platform-engineer's own servers. Nothing in the evidence pack mentions open-sourcing the runtime, self-hosting instructions, or on-prem deployment options; all references (dashboard, API, CLI, hosted MCP, SIP trunking) point to ElevenLabs-operated infrastructure.

   • [claimed-docs] “ElevenLabs Agents are managed either through the ElevenAgents dashboard, the ElevenLabs API, the Agents CLI or the hosted MCP server.”
   • [claimed-docs] “an assistant such as Claude can create, configure, and manage the agents in your workspace through natural language, with nothing to install…”
   • [claimed-docs] “create, configure, and manage the agents in your workspace through natural language, with nothing to install or run locally”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y @elevenlabs/cli --version` printed `elevenlabs 1.1.0` keylessly — the official ElevenLabs CLI (…”
   • [probe] “PROBE runtime (recorded 2026-09-05): keyless JSON-RPC initialize POST to the hosted MCP server https://api.elevenlabs.io/v1/mcp returned HTT…”

   proof ↗⚑ flag

   Bland—none0/10

   Bland is presented throughout as a hosted enterprise SaaS platform (managed infrastructure, release adoption controls, canary deployments on Bland's own containers) with no mention of open-source code or a self-hostable runtime; a community post even shows a user asking for an open-source alternative because Bland itself isn't one.

   • [claimed-docs] “You choose when to adopt a new release. Your infrastructure stays on the version you've selected until you decide to move forward.”
   • [claimed-docs] “Bland supports canary deployments — a way to run a new release on a separate set of containers alongside your production infrastructure and …”
   • [community] “I want to experiment with building my own phone agent. Currently experimented with bland.ai but it gets expensive. Any open source alternati…”

   proof ↗⚑ flag

Latency

1. platform-engineerSee documented end-to-end voice latency numbers or tuning guidance backing the platform's speed claims

   weight 3 · round to ElevenLabs Agents
   ElevenLabs Agents~partialclaimed4/10

   ElevenLabs markets 'ultra-low latency' (docs-37) and does document conversation-flow tuning knobs like turn-taking, interruption handling and timeouts (docs-33, docs-42, docs-19/49/54), which counts as tuning guidance for latency/turn-taking behavior. However there are no documented end-to-end latency benchmark numbers (e.g., ms figures, percentile breakdowns) anywhere in the evidence pack. Missing for 10: concrete latency benchmark numbers/metrics, methodology for measuring end-to-end latency, and any independent corroboration of the 'ultra-low latency' marketing claim.

   • [claimed-docs] “Configure, deploy and monitor natural, human-sounding agents in 70+ languages with leading accuracy and ultra-low latency across voice or ch…”
   • [claimed-docs] “Conversation flow settings determine how your assistant handles periods of user silence, interruptions during speech, and turn-taking behavi…”
   • [claimed-docs] “Configure turn-taking, interruptions, and timeout settings”
   • [claimed-docs] “The Max conversation duration setting limits the total time a conversation can remain active. ... The default is 600 seconds (10 minutes).”

   proof ↗⚑ flag

   Bland—none0/10

   No evidence pack items mention latency numbers, benchmarks, or tuning guidance for end-to-end voice response time; documentation covers pathways, SIP, MCP, CLI, and enterprise features but nothing about speed/latency metrics or optimization guidance.

   ⚑ flag

Turn taking

1. developerRely on the agent to handle interruptions (barge-in) gracefully — stopping speech, updating context, and recovering the turn

   weight 3 · round to ElevenLabs Agents
   ElevenLabs Agents~partialclaimed6/10

   Docs explicitly document configuring turn-taking, interruptions, and timeout settings, and conversation-flow settings that 'determine how your assistant handles periods of user silence, interruptions during speech, and turn-taking behavior,' confirming barge-in handling is a first-class configurable feature. However, evidence lacks detail on how context is updated after an interruption or how turn recovery specifically behaves, and there's no hands-on/independent validation of graceful recovery in practice. Missing for 10: technical detail on context truncation/update after interruption, independent or hands-on testing confirming graceful recovery, and latency benchmarks for barge-in specifically.

   • [claimed-docs] “Conversation flow settings determine how your assistant handles periods of user silence, interruptions during speech, and turn-taking behavi…”
   • [claimed-docs] “Configure turn-taking, interruptions, and timeout settings”
   • [claimed-docs] “The Max conversation duration setting limits the total time a conversation can remain active. ... The default is 600 seconds (10 minutes).”

   proof ↗⚑ flag

   Bland—none0/10

   The evidence pack covers pathways, webhooks, batch calls, evals, MCP/CLI integrations, and infrastructure features, but contains no mention of barge-in, interruption handling, stopping TTS mid-utterance, or turn recovery logic — a core voice-agent capability that would be a fair and expected axis for this product type. missing for 10: any documentation or claim about detecting user interruptions, halting agent speech, updating context after a barge-in, and resuming/recovering the conversational turn.

   ⚑ flag

2. developerEnable noise suppression or audio filtering so the agent stays coherent on noisy real-world calls

   weight 1 · round drawn
   ElevenLabs Agents—none0/10

   The evidence pack covers voice customization, turn-taking/interruption settings, tools, testing, and telephony (SIP) integration, but nowhere mentions noise suppression, background noise handling, or audio filtering for noisy real-world calls. This is a fair capability to expect for a voice-agent platform handling phone calls, but no documentation or feature reference confirms it exists.

   • [claimed-docs] “Conversation flow settings determine how your assistant handles periods of user silence, interruptions during speech, and turn-taking behavi…”
   • [claimed-docs] “Configure turn-taking, interruptions, and timeout settings”
   • [claimed-docs] “This integration enables all customers to use their existing phone systems while leveraging ElevenLabs' advanced voice AI capabilities.”

   proof ↗⚑ flag

   Bland—none0/10

   No evidence pack item mentions noise suppression, audio filtering, or handling of noisy real-world call environments; documentation covers pathways, integrations, MCP, CLI, SIP, and enterprise release features but nothing about audio quality/noise handling.

   ⚑ flag

3. developerUse model-based end-of-turn detection beyond simple VAD silence timeouts so the agent doesn't talk over slow speakers

   weight 2 · round drawn
   ElevenLabs Agents—none0/10

   Docs mention configurable turn-taking, interruption handling, and timeout settings (docs-33, docs-42), but nothing in the evidence describes a model-based end-of-turn detector that goes beyond a VAD/silence-timeout mechanism — no mention of semantic or ML-based turn-completion detection to avoid interrupting slow speakers. Missing for 10: any documentation of a dedicated end-of-turn prediction model, benchmarks or comparisons versus simple VAD timeouts, or hands-on evidence that the agent waits appropriately for slow speakers.

   • [claimed-docs] “Conversation flow settings determine how your assistant handles periods of user silence, interruptions during speech, and turn-taking behavi…”
   • [claimed-docs] “Configure turn-taking, interruptions, and timeout settings”
   • [claimed-docs] “The Max conversation duration setting limits the total time a conversation can remain active. ... The default is 600 seconds (10 minutes).”

   proof ↗⚑ flag

   Bland—none0/10

   The evidence pack contains no mention of turn-taking, end-of-turn detection, VAD, or interruption handling mechanisms of any kind — nothing addresses how Bland decides when a speaker has finished talking. This is a fair and applicable axis for a voice AI platform, but no capability is documented; missing for 10: any mention of end-of-turn/turn-taking model, VAD configuration, or handling of slow speakers/pauses.

   ⚑ flag

1. ai-native userDo everything through the API that I can do in the UI

   weight 2 · round to Bland
   ElevenLabs Agents~partialprobed7/10

   Docs show agents can be created, configured, and managed via the API, CLI, dashboard, or hosted MCP server, and quickstart explicitly says agents can be created 'via the API or the web dashboard,' implying broad parity across surfaces (elevenlabs-agents-docs-1, docs-39, docs-44). However, there's no explicit feature-by-feature confirmation that every dashboard capability (e.g., visual workflow builder, analytics dashboards, testing UI) has a full API equivalent, nor independent verification of parity. Missing for 10: an explicit parity statement or changelog confirming all dashboard features (workflow builder, analytics, testing suite) are fully scriptable via API, and independent/hands-on confirmation of that parity.

   • [claimed-docs] “ElevenLabs Agents are managed either through the ElevenAgents dashboard, the ElevenLabs API, the Agents CLI or the hosted MCP server.”
   • [claimed-docs] “we'll start by creating an agent via the API or the web dashboard”
   • [claimed-docs] “create, configure, and manage the agents in your workspace through natural language, with nothing to install or run locally”
   • [claimed-docs] “Build multi-step workflows with visual workflow builder”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y @elevenlabs/cli --version` printed `elevenlabs 1.1.0` keylessly — the official ElevenLabs CLI (…”

   proof ↗⚑ flag

   Bland✓fullprobed7/10

   Bland's docs show extensive programmatic control mirroring UI features: pathway creation/versioning, testbed, evals, batch calls, webhooks, SIP/phone number management, and a CLI/MCP server that explicitly lets users 'manage your entire Bland account from the terminal' and perform the same actions (calls, pathways, agents, analytics) as the UI. This breadth strongly supports API parity, though there's no explicit first-party statement guaranteeing 100% feature parity and no discoverable OpenAPI spec (all candidate URLs 404), so full parity isn't independently confirmed. Missing for 10: an explicit parity guarantee/documentation and a machine-readable OpenAPI spec, plus independent hands-on confirmation that every UI action has an API equivalent.

   • [claimed-docs] “See the Pathways API reference to create and manage pathways programmatically.”
   • [claimed-docs] “Manage your entire Bland account from the terminal: make calls, build and test pathways, configure phone numbers, and more.”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The testbed lets you take any call ... isolate a specific node interaction, edit the prompt, and run it multiple times to see how the output…”
   • [claimed-docs] “Batch calls let you initiate high-volume call campaigns by uploading a list of recipients via CSV.”
   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges.”
   • [probe] “official CLI documented at https://docs.bland.ai/sdks/cli”
   • [probe] “PROBE runtime (recorded 2026-09-05): `npx -y bland-cli --version` printed `0.6.2` keylessly — the official npm CLI (bland-cli, with dev term…”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.bland.ai/openapi.json, https://docs.bland.ai/swagger.json, https://docs.bland.ai/api/op…”

   proof ↗⚑ flag

2. ai-native userExport all of my data in open formats and leave

   weight 3 · round drawn
   ElevenLabs Agents—none0/10

   No evidence describes a data export feature, open-format data dumps, or account deletion/portability tooling; only conversation retention settings are documented, not export mechanisms. Missing for 10: any documented export API/CLI command, supported open export formats (e.g., JSON/CSV), and confirmation of full data portability/account closure process.

   ⚑ flag

   Bland—none0/10

   No evidence of a data export feature or open-format export of account data (pathways, call logs, memories, etc.); the docs cover CLI, MCP, and SDK integrations but nothing about exporting user data for portability/exit.

   ⚑ flag

3. ai-native userRead the product's source under an open license

   weight 2 · round drawn
   ElevenLabs Agents—none0/10

   ElevenLabs Agents is a closed, proprietary SaaS product; the CLI is open on npm but no evidence indicates the core Agents platform/backend source is available under an open license. No license file, GitHub repo, or open-source statement is present in the evidence pack.

   ⚑ flag

   Bland—none0/10

   Bland is a closed, commercial SaaS voice AI platform; there is no evidence of any open-source license or public source code repository. A community comment explicitly asks for an open-source alternative, implying Bland itself is not open source. This is an applicable axis (a product could publish open-source components) but no evidence supports it.

   • [community] “I want to experiment with building my own phone agent. Currently experimented with bland.ai but it gets expensive. Any open source alternati…”

   proof ↗⚑ flag

4. ai-native userSelf-host the core product

   weight 3 · round drawn
   ElevenLabs Agents—none0/10

   ElevenLabs Agents is entirely a hosted SaaS product—managed via dashboard, API, CLI, and hosted MCP server, all pointing to ElevenLabs' own infrastructure; there is no evidence of an open-source release, downloadable server binary, or self-hosting option for the core voice-agent runtime. missing for 10: any open-source repo or self-hosted deployment package, docs describing running the core service on one's own infrastructure, independent confirmation of self-hosting.

   • [claimed-docs] “ElevenLabs Agents are managed either through the ElevenAgents dashboard, the ElevenLabs API, the Agents CLI or the hosted MCP server.”
   • [claimed-docs] “an assistant such as Claude can create, configure, and manage the agents in your workspace through natural language, with nothing to install…”
   • [claimed-docs] “create, configure, and manage the agents in your workspace through natural language, with nothing to install or run locally”

   proof ↗⚑ flag

   Bland—none0/10

   Bland is documented as a hosted enterprise voice AI platform (call dispatch, pathways, SIP, MCP, CLI) with no mention of an on-prem/self-hosted deployment option; 'enterprise' release controls (docs-14, docs-29, docs-31) only govern version adoption timing on Bland's own infrastructure, not customer self-hosting. A community post explicitly looks for an open-source self-hostable alternative because Bland itself doesn't offer this.

   • [claimed-docs] “You choose when to adopt a new release. Your infrastructure stays on the version you've selected until you decide to move forward.”
   • [claimed-docs] “Bland supports canary deployments — a way to run a new release on a separate set of containers alongside your production infrastructure and …”
   • [claimed-docs] “You choose when to adopt a new release... Bland supports canary deployments — a way to run a new release on a separate set of containers alo…”
   • [community] “I want to experiment with building my own phone agent. Currently experimented with bland.ai but it gets expensive. Any open source alternati…”

   proof ↗⚑ flag

Pricing

1. founderSee published per-minute or usage pricing and estimate cost per call before committing

   weight 2 · round drawn
   ElevenLabs Agents—none0/10

   No evidence item in the pack references pricing, per-minute rates, usage-based billing, or cost calculators for ElevenLabs Agents; all citations are product/docs feature descriptions. Since founders evaluating a voice-agent SaaS could reasonably expect published pricing, the axis applies but is entirely unaddressed in this evidence pack.

   ⚑ flag

   Bland—none0/10

   No evidence pack item shows published per-minute or usage pricing, a pricing page, or any cost calculator; the only pricing-adjacent mention is a community complaint that Bland 'gets expensive' with no figures. Missing for 10: published price list, per-minute rate documentation, cost calculator or estimator tool.

   • [community] “I want to experiment with building my own phone agent. Currently experimented with bland.ai but it gets expensive. Any open source alternati…”

   proof ↗⚑ flag

1. ai-native userChoose where my data is stored (region/residency)

   weight 2 · round drawn
   ElevenLabs Agents—none0/10

   Evidence covers data retention duration settings, HIPAA/BAA eligibility, and infrastructure details, but nothing addresses selectable data region/residency options for storage location. missing for 10: any mention of region selection, data residency controls, or geographic storage options.

   • [claimed-docs] “Retention settings allow you to configure how long your conversational agent stores conversation transcripts and audio recordings.”
   • [claimed-docs] “Retention settings allow you to configure how long your conversational agent stores conversation transcripts and audio recordings”
   • [claimed-docs] “ElevenLabs Agents is one of ElevenLabs' HIPAA-eligible services, and we offer Business Associate Agreements (BAAs) to eligible customers”

   proof ↗⚑ flag

   Bland—none0/10

   No evidence in the pack mentions data residency, regional hosting options, or geographic storage controls for Bland; enterprise/infra docs discuss release versioning and SIP/canary deployments but not region selection. Missing for 10: any mention of data residency options, region-specific hosting, or compliance-driven storage location controls.

   ⚑ flag

2. ai-native userPrevent my data from being used to train AI models

   weight 3 · round drawn
   ElevenLabs Agents—none0/10

   The evidence pack covers data retention settings (how long transcripts/audio are stored) and HIPAA/BAA compliance, but nowhere mentions an explicit opt-out or policy regarding use of customer data/conversations for AI model training. missing for 10: explicit training-data opt-out policy or setting, terms-of-service language on model training use, any statement distinguishing enterprise vs free-tier data usage for training.

   • [claimed-docs] “Retention settings allow you to configure how long your conversational agent stores conversation transcripts and audio recordings.”
   • [claimed-docs] “Retention settings allow you to configure how long your conversational agent stores conversation transcripts and audio recordings”
   • [claimed-docs] “ElevenLabs Agents is one of ElevenLabs' HIPAA-eligible services, and we offer Business Associate Agreements (BAAs) to eligible customers”

   proof ↗⚑ flag

   Bland—none0/10

   No evidence in the pack addresses data-training opt-out or AI-training privacy policies for Bland; nothing about training-data usage or opt-out controls is mentioned across any of the docs or probes.

   ⚑ flag

3. ai-native userControl data retention and deletion

   weight 2 · round to ElevenLabs Agents
   ElevenLabs Agents~partialclaimed6/10

   Docs explicitly document configurable retention settings for conversation transcripts and audio recordings, giving users control over how long data is stored. However, evidence does not detail a deletion mechanism (e.g., data subject deletion requests, API/CLI commands to purge stored data) beyond retention duration settings. Missing for 10: explicit deletion/export controls, granular per-conversation deletion API, and independent verification of retention enforcement.

   • [claimed-docs] “Retention settings allow you to configure how long your conversational agent stores conversation transcripts and audio recordings.”
   • [claimed-docs] “Retention settings allow you to configure how long your conversational agent stores conversation transcripts and audio recordings”

   proof ↗⚑ flag

   Bland—none0/10

   No evidence pack items address data retention policies, data deletion controls, or privacy/compliance settings for call recordings, transcripts, or memory data. This axis clearly applies to an enterprise voice AI platform handling call data, but nothing in the evidence documents retention periods, deletion APIs, or GDPR/CCPA-style data controls. Missing for 10: retention policy docs, data deletion API/endpoint, compliance certifications, memory/data purge mechanism.

   ⚑ flag

4. ai-native userOpt out of telemetry and usage tracking

   weight 2 · round drawn
   ElevenLabs Agents—none0/10

   The evidence pack covers conversation transcript/audio retention settings (docs-26, docs-52) but nothing about opting out of product telemetry or usage tracking (e.g., CLI analytics, dashboard usage metrics) for the AI-native user. Retention configuration is about customer conversation data, not the vendor's own telemetry collection, so this axis has no supporting evidence.

   ⚑ flag

   Bland—none0/10

   No evidence pack items mention telemetry, analytics opt-out, or usage-tracking controls for Bland's own platform; this is a fair question for an enterprise SaaS product but is simply unaddressed in the evidence.

   ⚑ flag

Call control

1. developerEscalate a live call to a human with warm or blind transfer, passing context along

   weight 2 · round drawn
   ElevenLabs Agents—none0/10

   The evidence pack covers telephony features like SIP trunking, tools, webhooks, and MCP integrations, but nowhere documents call transfer (warm or blind) to a human agent or passing conversation context during handoff. Missing for 10: any mention of transfer_to_number/transfer_to_agent tool types, warm/blind transfer configuration, or context-passing during escalation.

   ⚑ flag

   Bland—none0/10

   The evidence pack covers pathways, webhooks, SIP, MCP, CLI, and other Bland features, but contains no mention of call transfer (warm or blind) or handing off a live call to a human agent with context. This is a standard telephony capability that could plausibly be documented, so absence of evidence yields 'none' rather than 'na'.

   ⚑ flag

2. developerMy agent can send DTMF keypresses, navigate IVR menus, and detect or leave voicemail

   weight 1 · round drawn
   ElevenLabs Agents—none0/10

   The evidence pack confirms ElevenLabs Agents supports telephony integration via SIP trunking and phone numbers, so the telephony axis clearly applies, but nothing in the docs mentions DTMF tone sending, IVR menu navigation, or voicemail detection/leaving capabilities.

   • [claimed-docs] “This integration enables all customers to use their existing phone systems while leveraging ElevenLabs' advanced voice AI capabilities.”
   • [claimed-docs] “Route calls to AI agents without changing your existing phone infrastructure”

   proof ↗⚑ flag

   Bland—none0/10

   The evidence pack covers pathways, SIP, webhooks, evals, and MCP/CLI tooling, but nowhere mentions DTMF keypress sending, IVR menu navigation, or voicemail detection/leaving — capabilities that are plausible for a voice telephony platform but are simply undocumented here.

   ⚑ flag

Campaigns

1. founderRun batch outbound call campaigns with scheduling and throughput controls

   weight 2 · round to Bland
   ElevenLabs Agents—none0/10

   The evidence pack documents phone-number/SIP trunking integration for routing calls to agents, but contains no mention of batch outbound calling, campaign scheduling, or throughput/rate controls for outbound call volume. Since telephony campaign management is a fair capability to expect from a voice-agent telephony product, absence of evidence here is 'none' rather than 'na'.

   • [claimed-docs] “This integration enables all customers to use their existing phone systems while leveraging ElevenLabs' advanced voice AI capabilities.”
   • [claimed-docs] “Route calls to AI agents without changing your existing phone infrastructure”

   proof ↗⚑ flag

   Bland✓fullclaimed8/10

   Bland's docs explicitly describe batch calls for uploading CSV recipient lists to run high-volume outbound campaigns, backed by scheduling/throughput-related infrastructure like SIP integration, phone number management, and analytics/evals to monitor campaign performance. Missing for 10: explicit documentation of scheduling controls (e.g., call windows/timing) and rate-limiting/throughput knobs specifically, plus independent hands-on verification of batch campaign behavior at scale.

   • [claimed-docs] “Batch calls let you initiate high-volume call campaigns by uploading a list of recipients via CSV.”
   • [claimed-docs] “Dispatch AI phone calls to call customers, leads, and to streamline operations.”
   • [claimed-docs] “Bland supports inbound and outbound SIP. Point calls from your carrier or PBX at Bland to be answered by an agent, or have Bland place calls…”
   • [claimed-docs] “Number porting to bring existing numbers to Bland”

   proof ↗⚑ flag

Numbers

1. developerProvision phone numbers and run both inbound and outbound calls through the platform's API

   weight 3 · round to Bland
   ElevenLabs Agents~partialclaimed6/10

   Docs confirm phone/telephony integration via SIP trunking, letting agents route inbound/outbound calls over existing phone infrastructure, and agents can be created/configured via the API. However, evidence does not explicitly show a documented API endpoint for provisioning new phone numbers or explicitly triggering outbound calls programmatically vs. via SIP trunk setup. Missing for 10: explicit phone-number provisioning API endpoint docs, explicit outbound-call-initiation API example, and independent/hands-on confirmation of inbound+outbound call flows.

   • [claimed-docs] “This integration enables all customers to use their existing phone systems while leveraging ElevenLabs' advanced voice AI capabilities.”
   • [claimed-docs] “Route calls to AI agents without changing your existing phone infrastructure”
   • [claimed-docs] “we'll start by creating an agent via the API or the web dashboard”

   proof ↗⚑ flag

   Bland✓fullprobed8/10

   Bland's docs explicitly document creating/managing inbound phone numbers, outbound call dispatch via API, bringing your own Twilio numbers, and SIP for both inbound/outbound, all programmatically accessible, plus a CLI/MCP that manage phone numbers and calls end-to-end. Missing for 10: no explicit REST API reference/OpenAPI spec confirmed (probe shows openapi.json 404s) and no independent hands-on developer report of a full provision+call round trip.

   • [claimed-docs] “Dispatch AI phone calls to call customers, leads, and to streamline operations.”
   • [claimed-docs] “Create inbound phone numbers for customer support, etc.”
   • [claimed-docs] “Users can connect their own Twilio account to Bland, easily bring over your existing phone numbers to use within the platform.”
   • [claimed-docs] “Number porting to bring existing numbers to Bland”
   • [claimed-docs] “Bland supports inbound and outbound SIP. Point calls from your carrier or PBX at Bland to be answered by an agent, or have Bland place calls…”
   • [claimed-docs] “Manage your entire Bland account from the terminal: make calls, build and test pathways, configure phone numbers, and more.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.bland.ai/openapi.json, https://docs.bland.ai/swagger.json, https://docs.bland.ai/api/op…”

   proof ↗⚑ flag

Sip

1. platform-engineerConnect my own carrier or PBX via SIP trunking (or import Twilio/Telnyx numbers) instead of being locked to bundled telephony

   weight 2 · round to Bland
   ElevenLabs Agents~partialclaimed7/10

   Docs explicitly describe a SIP trunking integration that lets customers connect their existing phone systems/carrier infrastructure to ElevenLabs agents without changing carriers, directly addressing the core ask of bringing your own PBX/carrier. However, the pack has no explicit mention of importing existing Twilio/Telnyx numbers or detailed carrier onboarding steps, and no independent/hands-on confirmation of a live SIP trunk setup working end-to-end. Missing for 10: explicit Twilio/Telnyx number import instructions, and third-party/hands-on validation of the SIP trunking flow.

   • [claimed-docs] “This integration enables all customers to use their existing phone systems while leveraging ElevenLabs' advanced voice AI capabilities.”
   • [claimed-docs] “Route calls to AI agents without changing your existing phone infrastructure”

   proof ↗⚑ flag

   Bland✓fullclaimed8/10

   Bland's docs explicitly document inbound/outbound SIP trunking to connect a customer's own carrier or PBX, plus number porting, and separately support connecting an existing Twilio account/numbers. Missing for 10: no mention of Telnyx import specifically and no independent/hands-on validation of SIP setup success.

   • [claimed-docs] “Bland supports inbound and outbound SIP. Point calls from your carrier or PBX at Bland to be answered by an agent, or have Bland place calls…”
   • [claimed-docs] “Number porting to bring existing numbers to Bland”
   • [claimed-docs] “Users can connect their own Twilio account to Bland, easily bring over your existing phone numbers to use within the platform.”

   proof ↗⚑ flag

Analytics

1. ai-native userThe platform's AI reviews my calls for me — scoring quality, flagging failures, and analyzing resolution automatically

   weight 2 · round to ElevenLabs Agents
   ElevenLabs Agents✓fullclaimed7/10

   Docs explicitly describe automated post-call analysis: 'Success evaluation' scores conversation quality/goal achievement, 'Sentiment analysis' flags user sentiment, and 'Data collection' extracts structured resolution details — directly matching the story's scoring/flagging/resolution-analysis ask. Missing for 10: no independent/hands-on validation of analysis accuracy, and no explicit 'failure flagging' terminology beyond success-criteria evaluation.

   • [claimed-docs] “Success evaluation: Define custom criteria to assess conversation quality, goal achievement, and customer satisfaction”
   • [claimed-docs] “Data collection: Extract specific data points from conversations such as contact information, issue details, or any structured information”
   • [claimed-docs] “Sentiment analysis: Understand user sentiment across completed conversations”
   • [claimed-docs] “Define custom criteria to assess conversation quality, goal achievement, and customer satisfaction”
   • [claimed-docs] “Extract structured information from conversations such as contact details and business data.”

   proof ↗⚑ flag

   Bland~partialclaimed7/10

   Bland's Evals feature explicitly lets users define LLM-judge agents that grade calls on custom dimensions (quality, resolution, etc.), and the Testbed lets you replay and analyze specific call nodes to spot failures — directly matching automated call review/scoring. However, this requires the user to configure eval criteria rather than being a fully out-of-the-box automatic analysis, and there's no evidence of a pre-built default 'failure flagging' report. Missing for 10: evidence of fully automatic, no-setup call scoring/dashboard, and independent/hands-on validation of eval accuracy.

   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges.”
   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges. You define eval agents, each of which grades one dimension of a call”
   • [claimed-docs] “The testbed lets you take any call ... isolate a specific node interaction, edit the prompt, and run it multiple times to see how the output…”
   • [claimed-docs] “The testbed lets you take any call — whether it's a historical call from call logs or a test chat from the pathway editor — isolate a specif…”

   proof ↗⚑ flag

2. founderSee call analytics — success rates, durations, outcomes, sentiment — in dashboards without building my own

   weight 2 · round to ElevenLabs Agents
   ElevenLabs Agents~partialclaimed6/10

   Docs confirm built-in agent-analysis features—success evaluation, sentiment analysis, data extraction, and conversation search—managed via the ElevenAgents dashboard, which covers most of the story's asked-for metrics without custom-building analytics. However, there's no explicit evidence of a dashboard showing aggregated success-rate percentages or call-duration analytics/rollups across calls, only per-conversation criteria and settings. Missing for 10: dashboard screenshots/evidence of aggregate success-rate metrics, call-duration reporting/rollups, and independent corroboration that these appear in a unified analytics view.

   • [claimed-docs] “Success evaluation: Define custom criteria to assess conversation quality, goal achievement, and customer satisfaction”
   • [claimed-docs] “Data collection: Extract specific data points from conversations such as contact information, issue details, or any structured information”
   • [claimed-docs] “Sentiment analysis: Understand user sentiment across completed conversations”
   • [claimed-docs] “Searching conversations: Find messages by keyword or meaning across your conversation history.”
   • [claimed-docs] “ElevenLabs Agents are managed either through the ElevenAgents dashboard, the ElevenLabs API, the Agents CLI or the hosted MCP server.”
   • [claimed-docs] “Extract structured information from conversations such as contact details and business data.”

   proof ↗⚑ flag

   Bland~partialprobed4/10

   Evidence confirms Bland has an analytics layer (MCP server can 'query analytics') and quality-grading tools like Evals (LLM judges scoring call dimensions) and a testbed for reviewing call interactions, implying some built-in metrics exist. However, there is no direct evidence of an actual dashboard UI showing success rates, call durations, outcomes, or sentiment trends over time — analytics access shown is via MCP/API query rather than a visual dashboard. Missing for 10: screenshots or docs of a native analytics dashboard, explicit mention of success-rate/duration/sentiment metrics, and independent confirmation the dashboard requires no custom building.

   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges. You define eval agents, each of which grades one dimension of a call”
   • [claimed-docs] “The testbed lets you take any call — whether it's a historical call from call logs or a test chat from the pathway editor — isolate a specif…”
   • [probe] “official MCP server documented at https://docs.bland.ai/integrations/mcp/overview”

   proof ↗⚑ flag

Monitoring

1. platform-engineerMonitor live calls in production and get alerts when agents misbehave or error rates spike

   weight 1 · round drawn
   ElevenLabs Agents~partialclaimed4/10

   Docs show post-call analytics (sentiment analysis, success evaluation, data extraction, conversation search) and a marketing claim to 'monitor' agents, but there is no evidence of live/real-time call monitoring dashboards, error-rate metrics, or alerting/notification mechanisms for misbehaving agents. Missing for 10: real-time monitoring dashboard, error-rate/latency metrics, alerting/notification system, on-call integration (e.g., PagerDuty/Slack alerts).

   • [claimed-docs] “Success evaluation: Define custom criteria to assess conversation quality, goal achievement, and customer satisfaction”
   • [claimed-docs] “Sentiment analysis: Understand user sentiment across completed conversations”
   • [claimed-docs] “Searching conversations: Find messages by keyword or meaning across your conversation history.”
   • [claimed-docs] “Define custom criteria to assess conversation quality, goal achievement, and customer satisfaction”
   • [claimed-docs] “Configure, deploy and monitor natural, human-sounding agents in 70+ languages with leading accuracy and ultra-low latency across voice or ch…”

   proof ↗⚑ flag

   Bland~partialclaimed4/10

   Bland provides post-call webhooks, evals with LLM judges, and an MCP integration that can 'query analytics' — giving some after-the-fact quality/analytics visibility — but there is no documented live-call monitoring dashboard, real-time alerting, or error-rate-spike notification system in the evidence pack. Missing for 10: live/real-time call monitoring UI, configurable alert thresholds, error-rate spike detection/paging, independent confirmation these exist in production.

   • [claimed-docs] “Post-call webhooks are HTTP notifications that Bland AI automatically sends to your server after a phone call completes.”
   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges.”
   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges. You define eval agents, each of which grades one dimension of a call”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The testbed lets you take any call — whether it's a historical call from call logs or a test chat from the pathway editor — isolate a specif…”

   proof ↗⚑ flag

Testing

1. developerTest agents with simulated conversations or evals before putting them on real phone calls

   weight 2 · round drawn
   ElevenLabs Agents✓fullclaimed8/10

   ElevenLabs documents an explicit Agent Testing feature that verifies conversational responses, tool usage, and full multi-turn outcomes before deployment, including converting real conversations into simulated test cases, plus success-evaluation criteria for assessing conversation quality/goal achievement — directly matching the pre-deployment simulated testing story. Missing for 10: no independent/hands-on report validating test accuracy or CI-integration details, and no explicit mention of automated eval scoring pipelines beyond docs description.

   • [claimed-docs] “Agent testing lets you verify conversational responses, tool usage, and full multi-turn outcomes before you deploy.”
   • [claimed-docs] “Transform real conversations into test cases when you find an interaction where the agent underperformed.”
   • [claimed-docs] “Agent testing lets you verify conversational responses, tool usage, and full multi-turn outcomes before you deploy”
   • [claimed-docs] “Success evaluation: Define custom criteria to assess conversation quality, goal achievement, and customer satisfaction”
   • [claimed-docs] “Define custom criteria to assess conversation quality, goal achievement, and customer satisfaction”

   proof ↗⚑ flag

   Bland✓fullclaimed8/10

   Bland provides explicit pre-production testing tools: Evals (LLM-judge grading of call quality), the Testbed (replay/edit/re-run node interactions on historical or test chats), staging promotion and draft-vs-production pathway separation, and canary/A/B rollout testing against real calls before full deployment. Together these let a developer simulate conversations and grade agent behavior before real phone calls go live. missing for 10: no independent/hands-on evidence of eval accuracy or testbed usage from outside vendor docs, and no explicit description of a pure text-based conversation simulator separate from testbed/staging.

   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges.”
   • [claimed-docs] “The testbed lets you take any call ... isolate a specific node interaction, edit the prompt, and run it multiple times to see how the output…”
   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges. You define eval agents, each of which grades one dimension of a call”
   • [claimed-docs] “The testbed lets you take any call — whether it's a historical call from call logs or a test chat from the pathway editor — isolate a specif…”
   • [claimed-docs] “You can also promote a version to staging to test it before it goes live, or send an individual call against a specific”
   • [claimed-docs] “When you edit a pathway, you are working on a draft. Live calls are not affected. They keep using the published production version.”
   • [claimed-docs] “This is how you A/B test a new agent release against your live production version, with real calls, before committing to a full rollout.”
   • [claimed-docs] “Bland supports canary deployments — a way to run a new release on a separate set of containers alongside your production infrastructure and …”

   proof ↗⚑ flag

Post call

1. developerExtract structured data from every call — outcomes, entities, dispositions — delivered via API or webhook after the call

   weight 2 · round drawn
   ElevenLabs Agents~partialclaimed6/10

   Docs show structured post-call outputs (data collection with contact/business data extraction, success-evaluation criteria, sentiment analysis) available via 'agent-analysis' features, and the platform is manageable via API. However, the pack never explicitly documents a webhook payload/callback delivering this structured analysis after a call, only webhook *tools* for in-call actions — missing for 10: explicit post-call webhook delivery mechanism/schema for outcomes and dispositions, and confirmation these analysis fields are retrievable via a documented API endpoint.

   • [claimed-docs] “Success evaluation: Define custom criteria to assess conversation quality, goal achievement, and customer satisfaction”
   • [claimed-docs] “Data collection: Extract specific data points from conversations such as contact information, issue details, or any structured information”
   • [claimed-docs] “Sentiment analysis: Understand user sentiment across completed conversations”
   • [claimed-docs] “Extract structured information from conversations such as contact details and business data”
   • [claimed-docs] “Extract structured information from conversations such as contact details and business data.”
   • [claimed-docs] “Tools give your assistant the ability to make external function calls to third party apps so you can get real-time information.”
   • [claimed-docs] “allow an assistant to trigger authenticated actions based on the conversation, like scheduling meetings or initiating order returns”

   proof ↗⚑ flag

   Bland~partialclaimed6/10

   Bland documents post-call webhooks that automatically deliver call data to a developer's server after each call, and evals let you grade/classify calls (dispositions) via LLM judges — both align with the API/webhook delivery and outcome-tagging parts of the story. However, there's no explicit documentation of structured entity extraction (e.g., named fields like names, dates, custom entities) as a distinct capability, nor a described webhook payload schema. Missing for 10: explicit entity-extraction feature docs, sample webhook payload showing structured outcome/entity/disposition fields, and independent confirmation of the data delivered.

   • [claimed-docs] “Post-call webhooks are HTTP notifications that Bland AI automatically sends to your server after a phone call completes.”
   • [claimed-docs] “Evals let you measure the quality of your calls with LLM judges. You define eval agents, each of which grades one dimension of a call”
   • [claimed-docs] “Give your agent instructions on how it should respond at specific points of the conversation. Choose between prompting or fixed sentences. E…”

   proof ↗⚑ flag

Tools

1. ai-native userMy voice agent can plug in MCP servers as tool sources so one integration grants it whole toolsets mid-call

   weight 2 · round to ElevenLabs Agents
   ElevenLabs Agents✓fullclaimed8/10

   Docs explicitly describe connecting conversational agents to external MCP servers as a tool source, enabling access to data/processing capabilities via that server mid-call, directly matching the story. Missing for 10: independent hands-on confirmation of mid-call multi-tool retrieval from a connected MCP server (only vendor docs, no third-party validation) and detail on how many tools/toolsets a single MCP connection exposes.

   • [claimed-docs] “ElevenLabs allows you to connect your conversational agents to external MCP servers. This enables your agents to: Access and process informa…”
   • [claimed-docs] “ElevenLabs allows you to connect your conversational agents to external MCP servers.”
   • [claimed-docs] “Access and process information from various data sources via the MCP server”

   proof ↗⚑ flag

   Bland—none0/10

   Bland ships an MCP *server* so coding agents can control the Bland account (docs-11/20/27, probe-3/probe-rt-2), which is the opposite role from what the story asks — the voice agent itself acting as an MCP *client* that plugs in external MCP servers as tool sources mid-call. Tool/function calling is documented only via custom HTTP API integrations (bland-docs-4, bland-docs-25), with no mention of the agent consuming MCP servers as a toolset source during calls.

   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [claimed-docs] “The Bland MCP server lets AI coding agents work with your Bland account through natural language: place and inspect calls, build and validat…”
   • [probe] “official MCP server documented at https://docs.bland.ai/integrations/mcp/overview”
   • [probe] “PROBE runtime (recorded 2026-09-05): keyless JSON-RPC initialize POST to the hosted MCP endpoint https://api.bland.ai/v1/mcp returned HTTP 4…”
   • [claimed-docs] “The API integration lets you connect your AI agent to any HTTP endpoint.”
   • [claimed-docs] “Connect external APIs and take live actions during phone calls.”

   proof ↗⚑ flag

2. developerMy agent can call external APIs and custom functions mid-conversation and speak the result without awkward dead air

   weight 3 · round to ElevenLabs Agents
   ElevenLabs Agents✓fullclaimed7/10

   Docs clearly describe multiple tool-calling mechanisms — webhook tools for external API calls, client-side tools, sandboxed custom JS, and MCP server integration — explicitly for making external function calls mid-conversation to fetch real-time info and trigger authenticated actions. Conversation-flow settings (turn-taking, interruption handling, ultra-low latency) address smooth conversational pacing, though not explicitly framed as avoiding dead air during tool execution. Missing for 10: explicit documentation of filler/hold-speech behavior while awaiting API results, and independent/hands-on verification that tool calls don't introduce audible pauses.

   • [claimed-docs] “Enable agents to call clients & APIs to perform actions”
   • [claimed-docs] “Tools executed directly on the client-side application (e.g., web browser, mobile app).”
   • [claimed-docs] “Tools give your assistant the ability to make external function calls to third party apps so you can get real-time information.”
   • [claimed-docs] “Custom JavaScript executed in a sandboxed environment on ElevenLabs' infrastructure.”
   • [claimed-docs] “ElevenLabs allows you to connect your conversational agents to external MCP servers. This enables your agents to: Access and process informa…”
   • [claimed-docs] “Tools allow ElevenLabs agents to perform actions beyond generating text responses.”
   • [claimed-docs] “allow an assistant to trigger authenticated actions based on the conversation, like scheduling meetings or initiating order returns”
   • [claimed-docs] “Conversation flow settings determine how your assistant handles periods of user silence, interruptions during speech, and turn-taking behavi…”
   • [claimed-docs] “Configure, deploy and monitor natural, human-sounding agents in 70+ languages with leading accuracy and ultra-low latency across voice or ch…”

   proof ↗⚑ flag

   Bland~partialclaimed6/10

   Bland's pathway/tools docs confirm agents can call external APIs and execute webhooks mid-conversation (bland-docs-4, bland-docs-25, bland-docs-30), which supports live function calling during a call. However, no evidence describes mechanisms for avoiding dead air (e.g., filler speech, streaming partial responses) while waiting on API results. missing for 10: explicit documentation of latency-masking/filler-speech behavior during API calls, and independent/hands-on confirmation of smooth conversational flow.

   • [claimed-docs] “The API integration lets you connect your AI agent to any HTTP endpoint.”
   • [claimed-docs] “Connect external APIs and take live actions during phone calls.”
   • [claimed-docs] “Give your agent instructions on how it should respond at specific points of the conversation. Choose between prompting or fixed sentences. E…”
   • [claimed-docs] “Conversational pathways are our new way of prompting Bland that has led to major breakthroughs in realism. Give your agent instructions on h…”

   proof ↗⚑ flag

Recording

1. platform-engineerRetrieve full call recordings and transcripts programmatically for every call

   weight 2 · round to ElevenLabs Agents
   ElevenLabs Agents~partialclaimed5/10

   Docs confirm that conversation transcripts and audio recordings are stored with configurable retention, and that conversations can be searched, analyzed, and have structured data extracted (docs-26/52, docs-21/23/56), implying underlying storage of call recordings/transcripts. However, there is no explicit documentation of a specific API endpoint or method for programmatically retrieving full call recordings and transcripts on a per-call basis. Missing for 10: an explicit 'get conversation' or 'get recording/transcript' API reference, example request/response showing programmatic export of audio + transcript per call.

   • [claimed-docs] “Retention settings allow you to configure how long your conversational agent stores conversation transcripts and audio recordings.”
   • [claimed-docs] “Retention settings allow you to configure how long your conversational agent stores conversation transcripts and audio recordings”
   • [claimed-docs] “Data collection: Extract specific data points from conversations such as contact information, issue details, or any structured information”
   • [claimed-docs] “Searching conversations: Find messages by keyword or meaning across your conversation history.”
   • [claimed-docs] “Extract structured information from conversations such as contact details and business data.”

   proof ↗⚑ flag

   Bland—none0/10

   No evidence pack item explicitly documents an API or endpoint for retrieving full call recordings or transcripts programmatically; references to call logs (testbed) and post-call webhooks hint at underlying data but never confirm a recording/transcript retrieval capability.

   ⚑ flag

Transcription

1. developerGet accurate real-time transcription with control over the STT provider, language models, or key terms

   weight 2 · round to ElevenLabs Agents
   ElevenLabs Agents~partialclaimed4/10

   Docs confirm agents let developers choose or bring custom LLMs (docs-41) and mention real-time voice interaction (docs-37), but there is no evidence of selectable STT providers, transcription accuracy tuning, or 'key terms'/vocabulary boosting controls anywhere in the pack. Missing for 10: STT provider selection, transcription-specific accuracy settings, key-term/glossary support, and any independent verification of transcription accuracy claims.

   • [claimed-docs] “Choose from supported LLMs or bring your own custom model”
   • [claimed-docs] “Configure, deploy and monitor natural, human-sounding agents in 70+ languages with leading accuracy and ultra-low latency across voice or ch…”
   • [claimed-docs] “Conversation flow settings determine how your assistant handles periods of user silence, interruptions during speech, and turn-taking behavi…”

   proof ↗⚑ flag

   Bland—none0/10

   Bland is a phone-call AI platform where transcription accuracy is clearly relevant, but the evidence pack contains no mention of STT provider selection, language model choice for transcription, or key-term/vocabulary boosting features. missing for 10: STT provider selection, transcription accuracy documentation, custom key terms/vocabulary support, language model configuration for transcription.

   ⚑ flag

Voices

1. founderClone a custom brand voice and use it for my agents, with a documented consent process

   weight 2 · round to Bland
   ElevenLabs Agents—none0/10

   The evidence pack describes selecting from 5k+ pre-built voices and voice customization (speed, multi-voice switching) but contains no mention of a voice-cloning feature for creating a custom brand voice, nor any documented consent process for cloning (e.g., voice verification, spoken consent statement) required by ElevenLabs' broader platform. Missing for 10: any docs on voice cloning/Instant or Professional Voice Cloning for Agents, and any consent-verification workflow.

   • [claimed-docs] “Select from 5k+ voices across 31 languages with customization options”
   • [claimed-docs] “Enable your agent to switch between different voices for multi-character conversations, storytelling, and language tutoring.”
   • [claimed-docs] “You can customize various aspects of your AI agent's voice to create a more natural and engaging conversation experience. This includes cont…”

   proof ↗⚑ flag

   Bland~partialclaimed4/10

   Bland documents voice cloning itself (a ~10-second clean sample sets the quality ceiling) which supports the 'clone a custom brand voice' half of the story, but no evidence describes a documented consent process, verification, or authorization requirement for cloning someone's voice. missing for 10: documented consent/verification workflow for voice cloning, legal/compliance guidance on brand-voice rights, independent confirmation of the cloning feature's fidelity.

   • [claimed-docs] “A clone needs one clean sample of about ten seconds. Quality of the sample sets the ceiling on quality of the voice”

   proof ↗⚑ flag

2. developerChoose from a broad voice library or plug in multiple TTS providers to get the voice I want

   weight 2 · round to ElevenLabs Agents
   ElevenLabs Agents~partialclaimed5/10

   Docs confirm a broad in-house voice library (5k+ voices across 31 languages, docs-6) with customization like speed, pronunciation, and multi-voice switching (docs-17/32/48), satisfying the first half of the story. However, there is no evidence of plugging in third-party/alternative TTS providers — the docs only mention bring-your-own LLM (docs-41), not bring-your-own TTS engine. Missing for 10: any documented support for external/third-party TTS provider integration, evidence of provider-agnostic voice pipeline, independent confirmation of using non-ElevenLabs voices.

   • [claimed-docs] “Select from 5k+ voices across 31 languages with customization options”
   • [claimed-docs] “Enable your agent to switch between different voices for multi-character conversations, storytelling, and language tutoring.”
   • [claimed-docs] “You can customize various aspects of your AI agent's voice to create a more natural and engaging conversation experience. This includes cont…”
   • [claimed-docs] “Enable your agent to switch between different voices for multi-character conversations, storytelling, and language tutoring”
   • [claimed-docs] “Choose from supported LLMs or bring your own custom model”

   proof ↗⚑ flag

   Bland~partialclaimed3/10

   Docs show voice cloning support (a 10-second sample sets voice quality) but there is no evidence of a broad pre-built voice library to choose from, nor any mention of plugging in multiple third-party TTS providers. Missing for 10: documented voice library/catalog, multi-provider TTS integration, and any comparison of voice options.

   • [claimed-docs] “A clone needs one clean sample of about ten seconds. Quality of the sample sets the ceiling on quality of the voice”

   proof ↗⚑ flag