ElevenLabs Agents vs Deepgram Voice Agent

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 ElevenLabs Agents
   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

   Deepgram Voice Agent~partialprobed5/10

   Deepgram documents agent creation via reusable agent configs (UUID-based), telephony connectivity for inbound/outbound calls, and a CLI with a built-in MCP server giving AI coding tools API access — the building blocks for programmatic provisioning exist. However, the evidence shows phone-call handling is done via a Twilio bridge (server code required) rather than a native Deepgram 'attach number/place call' API, and no doc shows the CLI or MCP server actually exposing agent-create + number-attach + call-place as a single end-to-end flow. Missing for 10: explicit CLI/MCP commands for provisioning a phone number and placing a call, and confirmation that this full workflow avoids external glue code/dashboard steps.

   • [claimed-docs] “Reusable Agent Configurations allow you to define and persist the agent block of your Settings message using the Deepgram API. Once created,…”
   • [claimed-docs] “Telephony: connect voice agents to phone networks for inbound and outbound calls.”
   • [claimed-docs] “The dg CLI includes a built-in MCP (Model Context Protocol) server that gives AI coding tools direct access to Deepgram APIs.”
   • [claimed-docs] “Twilio streams the call audio to your server, and your server bridges that audio to the Deepgram Voice Agent API, a single WebSocket that ru…”
   • [probe] “official MCP server documented at https://developers.deepgram.com/developer-tools/agentic-tools”
   • [probe] “official CLI documented at https://developers.deepgram.com/developer-tools/cli/getting-started”

   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

   Deepgram Voice Agent—none0/10

   Evidence covers building agents manually (prompting, function calling, reusable configs, MCP server for coding tools) but there is no mention of the platform's own AI generating or improving prompts, flows, or test cases from a description; the MCP server exposes Deepgram APIs to external coding assistants rather than being an AI author within the platform itself.

   • [claimed-docs] “Prompting: write system prompts that shape live-call behavior.”
   • [claimed-docs] “Reusable Agent Configurations allow you to define and persist the agent block of your Settings message using the Deepgram API. Once created,…”
   • [claimed-docs] “The dg CLI includes a built-in MCP (Model Context Protocol) server that gives AI coding tools direct access to Deepgram APIs.”
   • [probe] “official MCP server documented at https://developers.deepgram.com/developer-tools/agentic-tools”

   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 ElevenLabs Agents
   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

   Deepgram Voice Agent~partialclaimed6/10

   Deepgram provides all core building blocks — prompting, voice/LLM selection, telephony via Twilio bridging, and a single WebSocket API — that together could plausibly get a developer to a first live call quickly. However, the telephony path requires standing up your own server to bridge Twilio audio to the Voice Agent WebSocket, which is nontrivial integration work rather than a turnkey 'phone number in one hour' flow, and there's no first-party quickstart or time-to-first-call benchmark cited. missing for 10: an end-to-end quickstart/tutorial demonstrating full setup within an hour, evidence of a managed/no-code telephony number provisioning path, and independent hands-on confirmation of setup speed.

   • [claimed-docs] “Deepgram's Voice Agent API collapses that stack into a single, unified API. You open one WebSocket connection, send audio in, and receive au…”
   • [claimed-docs] “Telephony: connect voice agents to phone networks for inbound and outbound calls.”
   • [claimed-docs] “Prompting: write system prompts that shape live-call behavior.”
   • [claimed-docs] “Supported LLM providers | Parameter | open_ai | anthropic | aws_bedrock | google | groq | nvidia”
   • [claimed-docs] “Twilio streams the call audio to your server, and your server bridges that audio to the Deepgram Voice Agent API, a single WebSocket that ru…”
   • [claimed-docs] “That includes barge-in, the ability to talk over the agent and have it stop instantly.”

   proof ↗⚑ flag

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

   weight 2 · round to Deepgram Voice Agent
   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

   Deepgram Voice Agent~partialclaimed5/10

   Deepgram's docs confirm multilingual voice agent support, noting that STT and TTS model choices must be configured for the target language (deepgram-docs-10), but the evidence pack does not explicitly document automatic language detection or dynamic switching mid-call — only static multilingual configuration is described. Missing for 10: explicit documentation of automatic language detection, mid-call language switching mechanics, and independent/hands-on confirmation of this behavior in production.

   • [claimed-docs] “A multilingual voice agent has two model decisions: which STT model transcribes the user, and which TTS model speaks the agent.”

   proof ↗⚑ flag

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

   weight 2 · round to ElevenLabs Agents
   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

   Deepgram Voice Agent—none0/10

   All evidence describes a code/API-first architecture (WebSocket connections, JSON Settings messages, system prompts, function calling, CLI/SDK) rather than a visual no-code builder; there is no mention of a drag-and-drop flow designer, branching UI, or state-machine editor. missing for 10: any visual builder UI, no-code branching/state design, or drag-and-drop handoff configuration.

   • [claimed-docs] “Deepgram's Voice Agent API collapses that stack into a single, unified API. You open one WebSocket connection, send audio in, and receive au…”
   • [claimed-docs] “Multi-Agent Architecture: orchestrate multiple specialized agents that hand off based on context, intent, or domain.”
   • [claimed-docs] “Reusable Agent Configurations allow you to define and persist the agent block of your Settings message using the Deepgram API. Once created,…”
   • [claimed-docs] “Prompting: write system prompts that shape live-call behavior.”
   • [claimed-docs] “Supported LLM providers | Parameter | open_ai | anthropic | aws_bedrock | google | groq | nvidia”

   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

   Deepgram Voice Agent~partialclaimed6/10

   Deepgram supports prompting, reusable agent configs (passed at call time via UUID), function calling, and mid-call message injection, which together allow injecting per-call context/variables into agent behavior; A/B testing of configs also implies runtime parameterization. However there is no explicit documented mechanism for templated dynamic variables (e.g., {{caller_name}}) or a dedicated per-caller context API akin to other platforms. missing for 10: explicit dynamic variable templating syntax, dedicated per-caller metadata injection API, hands-on example of personalizing a call with caller-specific data.

   • [claimed-docs] “Reusable Agent Configurations allow you to define and persist the agent block of your Settings message using the Deepgram API. Once created,…”
   • [claimed-docs] “Prompting: write system prompts that shape live-call behavior.”
   • [claimed-docs] “Inject agent message | Mid-call | InjectAgentMessage | Makes the agent speak a specific line”
   • [claimed-docs] “A/B testing voices or prompts — Run two configurations in parallel and measure conversion, CSAT, or containment rate to pick a winner—no cod…”
   • [claimed-docs] “In the context of Deepgram Voice Agents, function calling enables your agent to perform tasks during a live conversation.”

   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

   Deepgram Voice Agent—none0/10

   The evidence pack covers architecture, function calling, multi-agent handoff, telephony, prompting, and LLM providers, but nowhere documents a built-in knowledge base or RAG feature for grounding the agent on user documents. Function calling ([deepgram-docs-2]) could theoretically be wired to an external retrieval system, but that is not the same as a built-in KB/RAG capability, and no such feature is described.

   • [claimed-docs] “In the context of Deepgram Voice Agents, function calling enables your agent to perform tasks during a live conversation.”
   • [claimed-docs] “Multi-Agent Architecture: orchestrate multiple specialized agents that hand off based on context, intent, or domain.”
   • [claimed-docs] “Prompting: write system prompts that shape live-call behavior.”

   proof ↗⚑ flag

Agent access

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

   weight 2 · round drawn
   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

   Deepgram Voice Agent✓fullprobed9/10

   Direct probe confirms llms.txt exists at developers.deepgram.com/llms.txt (HTTP 200) with explicit AI-agent instructions, and docs pages support .md suffix for clean markdown retrieval, both verified by live probes. missing for 10: no independent third-party confirmation of an agent actually consuming this in practice.

   • [probe] “PROBE llms.txt: HTTP 200 at https://developers.deepgram.com/llms.txt # Deepgram's Docs ## Instructions for AI Agents - For clean Markdown …”
   • [probe] “PROBE docs-md: HTTP 200 at https://developers.deepgram.com/docs/voice-agent.md > For clean Markdown of any page, append .md to the page URL.…”

   proof ↗⚑ flag

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

   weight 2 · round drawn
   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

   Deepgram Voice Agent~partialprobed6/10

   The Voice Agent is a WebSocket API (no GUI dependency) and Deepgram ships a terminal-based `dg` CLI for scripting Deepgram operations (transcribe, synthesize, manage account) which is inherently automatable/CI-friendly, and temporary tokens support secure automated auth. However, there is no explicit documentation of running the Voice Agent itself headlessly in CI pipelines, no CI/CD examples, and the CLI's primary use-cases described are transcription/synthesis rather than orchestrating full voice-agent sessions programmatically. Missing for 10: dedicated CI/headless automation guide for Voice Agent sessions, example CI pipeline configs, and confirmation the CLI can drive the Agent API end-to-end rather than just STT/TTS.

   • [claimed-docs] “Deepgram's Voice Agent API collapses that stack into a single, unified API. You open one WebSocket connection, send audio in, and receive au…”
   • [claimed-docs] “The dg CLI lets you transcribe files, stream live audio, synthesize speech, analyze text, and manage your Deepgram account from the terminal…”
   • [claimed-docs] “Unlike traditional API keys, temporary tokens are ideal for real-time applications requiring secure, temporary access to Deepgram's services…”
   • [probe] “official CLI documented at https://developers.deepgram.com/developer-tools/cli/getting-started”

   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

   Deepgram Voice Agent—none0/10

   Deepgram Voice Agent supports function calling for custom tool use, but there is no evidence the Voice Agent can act as an MCP client to consume external MCP servers' tools. The only MCP-related evidence is a built-in MCP *server* in the `dg` CLI that lets coding tools access Deepgram's APIs — the opposite direction of the story.

   • [claimed-docs] “In the context of Deepgram Voice Agents, function calling enables your agent to perform tasks during a live conversation.”
   • [claimed-docs] “The dg CLI includes a built-in MCP (Model Context Protocol) server that gives AI coding tools direct access to Deepgram APIs.”
   • [probe] “official MCP server documented at https://developers.deepgram.com/developer-tools/agentic-tools”

   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

   Deepgram Voice Agent✓fullprobed7/10

   Deepgram ships an official MCP server (bundled in the dg CLI) that lets AI coding tools/agents connect directly to Deepgram's APIs, confirmed both in docs and via a dedicated agentic-tools page. This directly satisfies the story of connecting an agent to Deepgram via an official MCP server. Missing for 10: independent/hands-on verification that the MCP server works reliably in practice, and more detail on which Voice Agent capabilities specifically are exposed through it.

   • [claimed-docs] “The dg CLI includes a built-in MCP (Model Context Protocol) server that gives AI coding tools direct access to Deepgram APIs.”
   • [probe] “official MCP server documented at https://developers.deepgram.com/developer-tools/agentic-tools”
   • [claimed-docs] “The dg CLI lets you transcribe files, stream live audio, synthesize speech, analyze text, and manage your Deepgram account from the terminal…”

   proof ↗⚑ flag

5. ai-native userUse an official CLI

   weight 2 · round to ElevenLabs Agents
   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

   Deepgram Voice Agent✓fullprobed8/10

   Deepgram ships an official 'dg' CLI for transcribing files, streaming audio, synthesizing speech, and managing accounts from the terminal, and it even embeds an MCP server for AI coding tools, directly supporting agentic/AI-native workflows. Missing for 10: independent/hands-on corroboration beyond first-party docs and more detail on CLI coverage of Voice Agent-specific features.

   • [claimed-docs] “The dg CLI lets you transcribe files, stream live audio, synthesize speech, analyze text, and manage your Deepgram account from the terminal…”
   • [claimed-docs] “The dg CLI includes a built-in MCP (Model Context Protocol) server that gives AI coding tools direct access to Deepgram APIs.”
   • [probe] “official CLI documented at https://developers.deepgram.com/developer-tools/cli/getting-started”
   • [probe] “official MCP server documented at https://developers.deepgram.com/developer-tools/agentic-tools”

   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

   Deepgram Voice Agent✓fullprobed9/10

   Deepgram publishes a documented public API (WebSocket-based Voice Agent API plus OpenAPI spec confirmed live), extensive docs covering endpoints, function calling, LLM providers, telephony, and auth, and even a CLI/MCP server for programmatic/agentic access, making it clearly drivable by an AI-native user. Missing for 10: independent third-party corroboration of API usage beyond vendor docs.

   • [claimed-docs] “Deepgram's Voice Agent API collapses that stack into a single, unified API. You open one WebSocket connection, send audio in, and receive au…”
   • [probe] “PROBE openapi: HTTP 200 at https://developers.deepgram.com/openapi.json — contains "openapi" key”
   • [claimed-docs] “The dg CLI lets you transcribe files, stream live audio, synthesize speech, analyze text, and manage your Deepgram account from the terminal…”
   • [claimed-docs] “The dg CLI includes a built-in MCP (Model Context Protocol) server that gives AI coding tools direct access to Deepgram APIs.”
   • [probe] “official CLI documented at https://developers.deepgram.com/developer-tools/cli/getting-started”
   • [claimed-docs] “Unlike traditional API keys, temporary tokens are ideal for real-time applications requiring secure, temporary access to Deepgram's services…”

   proof ↗⚑ flag

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

   weight 2 · round to Deepgram Voice Agent
   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

   Deepgram Voice Agent~partialclaimed4/10

   Deepgram documents temporary, short-lived tokens as an alternative to traditional API keys for secure, limited-duration access (deepgram-docs-14), which partially satisfies a least-privilege credential story, but there is no evidence of granular scopes/permissions (e.g., restricting a key to specific endpoints or agent capabilities) or role-based access control for agents. Missing for 10: documented scope/permission granularity, per-agent key restriction, and evidence of enforcement/verification of least-privilege in practice.

   • [claimed-docs] “Unlike traditional API keys, temporary tokens are ideal for real-time applications requiring secure, temporary access to Deepgram's services…”

   proof ↗⚑ flag

8. ai-native userBuild against official SDKs

   weight 2 · round to Deepgram Voice Agent
   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

   Deepgram Voice Agent~partialprobed5/10

   Evidence shows an official 'Browser Agent SDK' with composable packages, plus an OpenAPI spec, CLI, and MCP server for developer tooling, indicating some official SDK/build tooling exists. However, evidence does not mention broader server-side/language SDKs (Python, Node, Go, etc.) commonly expected for building agentic applications, nor independent corroboration of SDK quality. Missing for 10: multi-language SDK documentation, independent/hands-on developer reports, broader agentic build examples beyond browser.

   • [claimed-docs] “Browser Agent SDK: add voice AI to any web application via four composable packages.”
   • [probe] “PROBE openapi: HTTP 200 at https://developers.deepgram.com/openapi.json — contains "openapi" key”
   • [probe] “official CLI documented at https://developers.deepgram.com/developer-tools/cli/getting-started”
   • [probe] “official MCP server documented at https://developers.deepgram.com/developer-tools/agentic-tools”

   proof ↗⚑ flag

9. ai-native userSubscribe to events via webhooks

   weight 2 · round drawn
   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

   Deepgram Voice Agent—none0/10

   The evidence pack shows Deepgram Voice Agent operates via a persistent WebSocket connection for real-time audio streaming, not webhook-based event subscription; no documentation mentions webhooks for event notifications (e.g., call completion, transcript ready, errors). Absence of evidence for an applicable capability yields 'none'.

   ⚑ 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

   Deepgram Voice Agent—none0/10

   The evidence pack covers Deepgram Voice Agent's API capabilities (function calling, multi-agent orchestration, telephony, LLM providers, prompting) but contains no mention of an in-product analytics dashboard, conversation insights, or AI-generated suggestions derived from a user's own data. While such a feature (e.g., call analytics/insights) is plausible for a voice AI platform, no evidence shows Deepgram surfaces this.

   ⚑ flag

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

   weight 2 · round to ElevenLabs Agents
   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

   Deepgram Voice Agent—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

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

   weight 3 · round to ElevenLabs Agents
   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

   Deepgram Voice Agent—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ 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

   Deepgram Voice Agent✓fullclaimed8/10

   Deepgram Voice Agent is fundamentally a conversational system: users interact via natural spoken language, and the agent supports function calling to perform tasks, live prompting to shape behavior, and mid-call message injection—all driven by natural-language conversation rather than rigid commands. This is core, well-documented functionality (docs-2, docs-7, docs-11) directly matching the story's intent for an ai-native/agentic persona. Missing for 10: independent/hands-on validation of natural-language command accuracy and no evidence of complex multi-turn command chaining reliability from third-party sources.

   • [claimed-docs] “In the context of Deepgram Voice Agents, function calling enables your agent to perform tasks during a live conversation.”
   • [claimed-docs] “Prompting: write system prompts that shape live-call behavior.”
   • [claimed-docs] “Inject agent message | Mid-call | InjectAgentMessage | Makes the agent speak a specific line”
   • [claimed-docs] “Multi-Agent Architecture: orchestrate multiple specialized agents that hand off based on context, intent, or domain.”

   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

   Deepgram Voice Agent—none0/10

   Evidence shows an OpenAPI spec exists and docs pages are available as clean Markdown, but there is no evidence of an interactive API reference with runnable/try-it-now examples (e.g., embedded playground, live code execution, or Swagger/Postman-style explorer). Missing for 10: interactive explorer UI, runnable/executable code snippets, live request/response testing.

   • [probe] “PROBE openapi: HTTP 200 at https://developers.deepgram.com/openapi.json — contains "openapi" key”
   • [probe] “PROBE llms.txt: HTTP 200 at https://developers.deepgram.com/llms.txt # Deepgram's Docs ## Instructions for AI Agents - For clean Markdown …”
   • [probe] “PROBE docs-md: HTTP 200 at https://developers.deepgram.com/docs/voice-agent.md > For clean Markdown of any page, append .md to the page URL.…”

   proof ↗⚑ flag

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

   weight 2 · round to Deepgram Voice Agent
   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

   Deepgram Voice Agent✓fullprobed9/10

   A probe confirms Deepgram publishes a machine-readable OpenAPI spec at openapi.json containing the 'openapi' key, directly satisfying the story. Missing for 10: no first-party docs page explicitly announcing/describing the OpenAPI spec's coverage or versioning.

   • [probe] “PROBE openapi: HTTP 200 at https://developers.deepgram.com/openapi.json — contains "openapi" key”

   proof ↗⚑ flag

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

   weight 1 · round to ElevenLabs Agents
   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

   Deepgram Voice Agent—none0/10

   No evidence pack item mentions a sandbox environment, test/dev API keys, or a way to test without touching production data or usage; only production endpoints, EU endpoint, and opt-out flags are documented. Missing for 10: sandbox/test-mode environment, documentation of non-production keys or test credits, any guidance on isolating test traffic from production data.

   ⚑ flag

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

   weight 2 · round drawn
   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

   Deepgram Voice Agent—none0/10

   The evidence pack shows extensive Voice Agent API documentation, an OpenAPI spec, CLI, and MCP server, but contains no mention of API versioning scheme or a documented deprecation policy for the Voice Agent API.

   ⚑ flag

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

   weight 2 · round drawn
   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

   Deepgram Voice Agent—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

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

   weight 3 · round to ElevenLabs Agents
   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

   Deepgram Voice Agent~partialclaimed5/10

   Deepgram Voice Agent supports function calling that lets the agent trigger actions during a live conversation, and features like InjectAgentMessage and multi-agent handoff suggest some event-driven behavior, but this is developer-defined logic (via function calling code) rather than a declarative rules engine for automatically triggering actions on arbitrary events. missing for 10: a documented rules/trigger engine (if-this-then-that style), event subscription system, or automation workflow builder distinct from manual function-calling code.

   • [claimed-docs] “In the context of Deepgram Voice Agents, function calling enables your agent to perform tasks during a live conversation.”
   • [claimed-docs] “Multi-Agent Architecture: orchestrate multiple specialized agents that hand off based on context, intent, or domain.”
   • [claimed-docs] “Inject agent message | Mid-call | InjectAgentMessage | Makes the agent speak a specific line”

   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

   Deepgram Voice Agent—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

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

   weight 1 · round to Deepgram Voice Agent
   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

   Deepgram Voice Agent~partialclaimed3/10

   Deepgram's Reusable Agent Configurations let you persist a named agent config and reference it by UUID, and support A/B testing two configs in parallel, which is adjacent to versioning, but there is no documented version history, diff/review workflow, or explicit rollback mechanism. missing for 10: version history tracking, review/approval workflow, explicit rollback capability, changelog or diffing between config versions.

   • [claimed-docs] “Reusable Agent Configurations allow you to define and persist the agent block of your Settings message using the Deepgram API. Once created,…”
   • [claimed-docs] “A/B testing voices or prompts — Run two configurations in parallel and measure conversion, CSAT, or containment rate to pick a winner—no cod…”

   proof ↗⚑ flag

Compliance

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

   weight 2 · round drawn
   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

   Deepgram Voice Agent~partialclaimed4/10

   Deepgram documents general data-privacy controls (per-request opt-out from retention via mip_opt_out, EU-region processing endpoint) that touch on data retention/residency, but there is no explicit documentation of per-call recording toggles, consent/disclosure workflows, or configurable retention windows tied to call recordings specifically. Missing for 10: explicit per-call recording enable/disable controls, documented retention period configuration for stored call audio/transcripts, and consent/disclosure feature support.

   • [claimed-docs] “Opt out per request with mip_opt_out=true. Opted-out requests are not retained”
   • [claimed-docs] “For customers requiring data processing within the European Union, Deepgram provides an EU-specific endpoint at api.eu.deepgram.com.”

   proof ↗⚑ 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

   Deepgram Voice Agent~partialclaimed4/10

   Evidence shows EU data-residency endpoint and an opt-out from data retention/training, indicating some compliance/trust infrastructure, but there is no mention of HIPAA/BAA support or SOC 2 certification anywhere in the pack. missing for 10: HIPAA/BAA documentation, SOC 2 attestation evidence, broader regional residency options beyond EU.

   • [claimed-docs] “Opt out per request with mip_opt_out=true. Opted-out requests are not retained”
   • [claimed-docs] “For customers requiring data processing within the European Union, Deepgram provides an EU-specific endpoint at api.eu.deepgram.com.”

   proof ↗⚑ flag

Scale

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

   weight 2 · round drawn
   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

   Deepgram Voice Agent—none0/10

   No evidence pack item mentions concurrency limits, rate limits, per-account call caps, scaling guidance, or capacity request processes for the Voice Agent API; the docs cover architecture, features, and integrations but nothing about scale/concurrency documentation.

   ⚑ 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

   Deepgram Voice Agent—none0/10

   Deepgram Voice Agent is a hosted, cloud-only API accessed via WebSocket; nothing in the evidence indicates open-source runtime code that can be self-hosted on customer infrastructure. All references point to Deepgram-hosted endpoints (api.deepgram.com, api.eu.deepgram.com) and a CLI/SDK for calling the cloud service, not deploying the runtime itself.

   • [claimed-docs] “Deepgram's Voice Agent API collapses that stack into a single, unified API. You open one WebSocket connection, send audio in, and receive au…”
   • [claimed-docs] “For customers requiring data processing within the European Union, Deepgram provides an EU-specific endpoint at api.eu.deepgram.com.”
   • [claimed-docs] “Twilio streams the call audio to your server, and your server bridges that audio to the Deepgram Voice Agent API, a single WebSocket that ru…”

   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

   Deepgram Voice Agent—none0/10

   The evidence pack covers architecture, features (barge-in, telephony, function calling, multi-agent), and CLI/SDK tooling, but contains no documented latency numbers (e.g., ms end-to-end) or tuning guidance for reducing turn-taking latency. missing for 10: quantified latency benchmarks, latency tuning/configuration guidance, any performance dashboard or whitepaper on speed claims.

   • [claimed-docs] “Deepgram's Voice Agent API collapses that stack into a single, unified API. You open one WebSocket connection, send audio in, and receive au…”
   • [claimed-docs] “That includes barge-in, the ability to talk over the agent and have it stop instantly.”
   • [claimed-docs] “Twilio streams the call audio to your server, and your server bridges that audio to the Deepgram Voice Agent API, a single WebSocket that ru…”

   proof ↗⚑ 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 drawn
   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

   Deepgram Voice Agent~partialclaimed6/10

   Docs explicitly claim barge-in support where users can talk over the agent and it stops instantly, plus mid-call context tools like InjectAgentMessage for updating conversation state. However, evidence lacks detail on how turn recovery/context updating works technically after interruption, and there's no independent/hands-on corroboration of graceful recovery in practice. missing for 10: technical details on context-state recovery post-interruption, independent verification of barge-in reliability, edge-case handling (e.g., rapid interruptions, latency of stop).

   • [claimed-docs] “That includes barge-in, the ability to talk over the agent and have it stop instantly.”
   • [claimed-docs] “Inject agent message | Mid-call | InjectAgentMessage | Makes the agent speak a specific line”
   • [claimed-docs] “Deepgram's Voice Agent API collapses that stack into a single, unified API. You open one WebSocket connection, send audio in, and receive au…”

   proof ↗⚑ 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

   Deepgram Voice Agent—none0/10

   No evidence pack item mentions noise suppression, background noise filtering, or audio denoising features for the Voice Agent; coverage focuses on barge-in, telephony, multi-agent orchestration, and LLM/TTS configuration instead.

   ⚑ 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

   Deepgram Voice Agent—none0/10

   Evidence only mentions barge-in (interrupting agent mid-speech) and generic prompting/architecture docs; there is no mention of model-based end-of-turn detection or endpointing that goes beyond VAD/silence timeouts to avoid cutting off slow speakers.

   ⚑ flag

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

   weight 2 · round to ElevenLabs Agents
   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

   Deepgram Voice Agent~partialprobed6/10

   Deepgram is fundamentally API-first — the Voice Agent is a WebSocket API with full configuration (prompting, function calling, multi-agent, telephony, reusable configs) exposed programmatically, and even the CLI/MCP server let AI-native users manage things from the terminal or via agentic tools rather than a GUI. However, evidence doesn't show a full-featured UI to compare against (e.g., a dashboard/console) nor confirm that every console feature (like A/B testing dashboards, analytics views) has a documented API equivalent. missing for 10: explicit mapping of console/UI-only features (analytics dashboards, A/B test result views) to API endpoints, and confirmation no UI-exclusive functionality exists.

   • [claimed-docs] “Deepgram's Voice Agent API collapses that stack into a single, unified API. You open one WebSocket connection, send audio in, and receive au…”
   • [claimed-docs] “Reusable Agent Configurations allow you to define and persist the agent block of your Settings message using the Deepgram API. Once created,…”
   • [claimed-docs] “The dg CLI lets you transcribe files, stream live audio, synthesize speech, analyze text, and manage your Deepgram account from the terminal…”
   • [claimed-docs] “The dg CLI includes a built-in MCP (Model Context Protocol) server that gives AI coding tools direct access to Deepgram APIs.”
   • [claimed-docs] “A/B testing voices or prompts — Run two configurations in parallel and measure conversion, CSAT, or containment rate to pick a winner—no cod…”
   • [probe] “PROBE openapi: HTTP 200 at https://developers.deepgram.com/openapi.json — contains "openapi" key”
   • [probe] “official MCP server documented at https://developers.deepgram.com/developer-tools/agentic-tools”
   • [probe] “official CLI documented at https://developers.deepgram.com/developer-tools/cli/getting-started”

   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

   Deepgram Voice Agent—none0/10

   No evidence describes any data export, account data portability, or open-format export/deletion workflow letting a user take their data and leave; docs cover retention opt-out and EU processing but not export tooling. missing for 10: data export mechanism, open-format export documentation, account closure/data portability guarantees.

   ⚑ 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

   Deepgram Voice Agent—none0/10

   Deepgram Voice Agent is a proprietary cloud API/SDK product; no evidence anywhere in the pack points to an open-source license or public source repository for the core Voice Agent service. Only API docs, CLI, and SDK usage are documented, none of which imply open-licensed source availability.

   ⚑ 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

   Deepgram Voice Agent—none0/10

   Deepgram Voice Agent is a cloud API/SaaS product; all evidence points to hosted WebSocket endpoints, EU regional endpoints, and cloud-based configuration — there is no mention of a self-hostable or on-premises deployment package for the core voice agent model/inference stack. missing for 10: any self-hosting/on-prem deployment option, downloadable model weights or container, docs on running the core service outside Deepgram's cloud.

   • [claimed-docs] “Deepgram's Voice Agent API collapses that stack into a single, unified API. You open one WebSocket connection, send audio in, and receive au…”
   • [claimed-docs] “For customers requiring data processing within the European Union, Deepgram provides an EU-specific endpoint at api.eu.deepgram.com.”

   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

   Deepgram Voice Agent—none0/10

   No evidence pack item mentions pricing, per-minute rates, usage-based cost, or any pricing page/calculator for the Voice Agent product; all citations focus on architecture, features, and developer tools.

   ⚑ flag

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

   weight 2 · round to Deepgram Voice Agent
   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

   Deepgram Voice Agent~partialclaimed6/10

   Deepgram documents an EU-specific endpoint (api.eu.deepgram.com) for customers requiring EU data processing, and a per-request opt-out for model improvement data retention, showing some regional/data-handling control. However, this is limited to EU vs. default (US) with no evidence of broader region selection (e.g., APAC, other jurisdictions) or a general residency configuration option across the platform. Missing for 10: documented list of all available regions, self-serve region selection in dashboard/API config, and independent confirmation of residency guarantees beyond docs.

   • [claimed-docs] “For customers requiring data processing within the European Union, Deepgram provides an EU-specific endpoint at api.eu.deepgram.com.”
   • [claimed-docs] “Opt out per request with mip_opt_out=true. Opted-out requests are not retained”

   proof ↗⚑ flag

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

   weight 3 · round to Deepgram Voice Agent
   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

   Deepgram Voice Agent~partialclaimed6/10

   Deepgram documents a per-request opt-out (mip_opt_out=true) from its Model Improvement Program, with opted-out requests not retained, directly addressing training-data use. However, the evidence only shows a per-request flag rather than an account-wide default-off setting, and there's no independent verification that opt-out requests are truly excluded from training. Missing for 10: account/org-level opt-out default, third-party audit or independent confirmation of non-training use, and clarity on default behavior when the flag is omitted.

   • [claimed-docs] “Opt out per request with mip_opt_out=true. Opted-out requests are not retained”

   proof ↗⚑ 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

   Deepgram Voice Agent~partialclaimed5/10

   Deepgram documents a per-request opt-out (mip_opt_out=true) that prevents retention, and references data-privacy compliance including an EU-specific endpoint, giving users some retention control. However, there is no documented self-service deletion mechanism for already-retained data, no stated retention periods, and no dashboard/API for managing or purging stored voice data. Missing for 10: explicit data deletion API/console, documented retention duration policy, and confirmation of deletion for non-opted-out data.

   • [claimed-docs] “Opt out per request with mip_opt_out=true. Opted-out requests are not retained”
   • [claimed-docs] “For customers requiring data processing within the European Union, Deepgram provides an EU-specific endpoint at api.eu.deepgram.com.”

   proof ↗⚑ flag

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

   weight 2 · round to Deepgram Voice Agent
   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

   Deepgram Voice Agent~partialclaimed4/10

   Deepgram documents an opt-out mechanism (mip_opt_out=true) for model improvement/data retention on a per-request basis, which is a form of telemetry/usage-data opt-out, but this is narrowly scoped to training data retention rather than general telemetry/usage tracking (e.g., analytics, product usage metrics). Missing for 10: documentation of a broader telemetry/usage-tracking opt-out setting, confirmation this covers all usage data beyond model-improvement retention, and independent verification that opting out has no side effects.

   • [claimed-docs] “Opt out per request with mip_opt_out=true. Opted-out requests are not retained”

   proof ↗⚑ 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

   Deepgram Voice Agent—none0/10

   Evidence covers telephony connectivity, function calling, multi-agent handoff between AI agents, and mid-call message injection, but nothing documents a warm/blind transfer to a human agent (e.g., SIP REFER, call transfer function, or context handoff to a live operator).

   ⚑ 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

   Deepgram Voice Agent—none0/10

   The evidence covers telephony connectivity, function calling, barge-in, and multi-agent orchestration, but nothing documents DTMF keypress sending, IVR menu navigation, or voicemail detection/leaving capabilities.

   ⚑ flag

Campaigns

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

   weight 2 · round drawn
   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

   Deepgram Voice Agent—none0/10

   Deepgram's docs confirm outbound telephony connectivity (docs-5, docs-18) and per-call features like barge-in and prompting, but there is no mention of campaign-level batch dialing, call scheduling, or throughput/rate controls for outbound campaigns — this is a real-time conversation API, not a dialer/campaign manager.

   • [claimed-docs] “Telephony: connect voice agents to phone networks for inbound and outbound calls.”
   • [claimed-docs] “Twilio streams the call audio to your server, and your server bridges that audio to the Deepgram Voice Agent API, a single WebSocket that ru…”

   proof ↗⚑ flag

Numbers

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

   weight 3 · round drawn
   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

   Deepgram Voice Agent~partialclaimed6/10

   Docs confirm Deepgram Voice Agent supports inbound/outbound calls and telephony integration (e.g., via Twilio bridging audio to a single WebSocket), but there is no evidence Deepgram itself provisions or manages phone numbers—developers must bring their own Twilio account/number and build a bridging server. Missing for 10: native phone-number provisioning API, first-party telephony number management, and evidence of outbound call initiation directly through Deepgram's API without a third-party carrier.

   • [claimed-docs] “Telephony: connect voice agents to phone networks for inbound and outbound calls.”
   • [claimed-docs] “Twilio streams the call audio to your server, and your server bridges that audio to the Deepgram Voice Agent API, a single WebSocket that ru…”
   • [claimed-docs] “That includes barge-in, the ability to talk over the agent and have it stop instantly.”

   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 ElevenLabs Agents
   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

   Deepgram Voice Agent—none0/10

   Evidence only documents a generic WebSocket bridge pattern for Twilio (docs-18, docs-8) and a general 'Telephony' feature bullet (docs-5), but there is no mention of SIP trunking, PBX connectivity, or Telnyx number import — the specific mechanisms a platform engineer would need to bring their own carrier.

   • [claimed-docs] “Telephony: connect voice agents to phone networks for inbound and outbound calls.”
   • [claimed-docs] “Twilio streams the call audio to your server, and your server bridges that audio to the Deepgram Voice Agent API, a single WebSocket that ru…”
   • [claimed-docs] “That includes barge-in, the ability to talk over the agent and have it stop instantly.”

   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

   Deepgram Voice Agent—none0/10

   The evidence pack covers Deepgram Voice Agent's real-time conversation infrastructure (STT/TTS, function calling, telephony, prompting, A/B testing of voices/prompts) but contains no mention of post-call AI review, automated quality scoring, failure flagging, or resolution analysis of completed calls.

   ⚑ 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

   Deepgram Voice Agent—none0/10

   Deepgram is a developer API/infrastructure product; evidence shows mentions of A/B testing metrics (conversion, CSAT, containment rate) as data points but no evidence of an actual built-in dashboard for call analytics, success rates, durations, sentiment, or outcomes that founders can view without building their own.

   • [claimed-docs] “A/B testing voices or prompts — Run two configurations in parallel and measure conversion, CSAT, or containment rate to pick a winner—no cod…”

   proof ↗⚑ flag

Monitoring

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

   weight 1 · round to ElevenLabs Agents
   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

   Deepgram Voice Agent—none0/10

   No evidence of live monitoring dashboards, error-rate alerting, or observability/analytics tooling for production calls; docs cover agent architecture, function calling, telephony, and configuration but nothing about monitoring or alerting on agent misbehavior.

   ⚑ flag

Testing

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

   weight 2 · round to ElevenLabs Agents
   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

   Deepgram Voice Agent—none0/10

   The evidence covers live-call features (function calling, prompting, telephony, A/B testing of live configs) but nothing describes a simulated-conversation or eval framework for pre-production testing of agents before real phone calls. Missing for 10: any documentation of a test/sandbox mode, conversation simulation tool, or eval harness for agents.

   • [claimed-docs] “A/B testing voices or prompts — Run two configurations in parallel and measure conversion, CSAT, or containment rate to pick a winner—no cod…”
   • [claimed-docs] “Reusable Agent Configurations allow you to define and persist the agent block of your Settings message using the Deepgram API. Once created,…”

   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 to ElevenLabs Agents
   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

   Deepgram Voice Agent—none0/10

   The evidence pack covers live function calling during a call and various agent configuration/telephony features, but there is no mention of post-call structured data extraction (summaries, entities, dispositions) or webhook delivery of such analytics after a call ends.

   ⚑ 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

   Deepgram Voice Agent—none0/10

   Evidence confirms mid-call function calling exists for the Voice Agent (deepgram-docs-2) but only documents an MCP *server* built into the CLI that lets coding tools access Deepgram APIs (deepgram-docs-13, deepgram-probe-4) — this is the reverse direction, not the Voice Agent acting as an MCP client to pull in external toolsets mid-call. No evidence shows the Voice Agent itself can connect to MCP servers as a tool source.

   • [claimed-docs] “In the context of Deepgram Voice Agents, function calling enables your agent to perform tasks during a live conversation.”
   • [claimed-docs] “The dg CLI includes a built-in MCP (Model Context Protocol) server that gives AI coding tools direct access to Deepgram APIs.”
   • [probe] “official MCP server documented at https://developers.deepgram.com/developer-tools/agentic-tools”

   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

   Deepgram Voice Agent~partialclaimed6/10

   Docs confirm function calling is a supported feature for performing tasks mid-conversation (deepgram-docs-2), and the agent architecture is a single low-latency WebSocket for audio in/out (deepgram-docs-1) with barge-in and inject-message features (deepgram-docs-8, deepgram-docs-11) suggesting attention to latency/dead-air. However there's no explicit documentation or example describing how function-call latency is masked (e.g., filler speech, streaming partial results) while awaiting an API response. Missing for 10: concrete guidance/examples on avoiding dead air during function execution, sample code showing async function calls with the agent speaking a holding phrase, and independent/hands-on verification of smooth latency handling.

   • [claimed-docs] “In the context of Deepgram Voice Agents, function calling enables your agent to perform tasks during a live conversation.”
   • [claimed-docs] “Deepgram's Voice Agent API collapses that stack into a single, unified API. You open one WebSocket connection, send audio in, and receive au…”
   • [claimed-docs] “That includes barge-in, the ability to talk over the agent and have it stop instantly.”
   • [claimed-docs] “Inject agent message | Mid-call | InjectAgentMessage | Makes the agent speak a specific line”

   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

   Deepgram Voice Agent—none0/10

   The evidence pack covers Voice Agent architecture, function calling, telephony bridging, LLM providers, and data privacy opt-outs, but nowhere describes an API or mechanism to retrieve full call recordings or persisted transcripts after a call ends. No endpoint, storage feature, or retrieval workflow is documented for this specific capability.

   ⚑ flag

Transcription

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

   weight 2 · round to Deepgram Voice Agent
   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

   Deepgram Voice Agent~partialclaimed6/10

   Docs confirm real-time transcription via the unified WebSocket API, selectable LLM providers (docs-9), and multilingual STT/TTS model choices (docs-10), giving developers meaningful control over STT and LLM models. However, there is no evidence of 'key terms' or keyword-boosting controls specific to the Voice Agent's transcription pipeline. Missing for 10: explicit key-term/keyword-boost configuration support, independent benchmarking of transcription accuracy.

   • [claimed-docs] “Deepgram's Voice Agent API collapses that stack into a single, unified API. You open one WebSocket connection, send audio in, and receive au…”
   • [claimed-docs] “Supported LLM providers | Parameter | open_ai | anthropic | aws_bedrock | google | groq | nvidia”
   • [claimed-docs] “A multilingual voice agent has two model decisions: which STT model transcribes the user, and which TTS model speaks the agent.”

   proof ↗⚑ flag

Voices

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

   weight 2 · round drawn
   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

   Deepgram Voice Agent—none0/10

   No evidence of a voice cloning feature or consent process; docs cover STT/TTS model selection, multilingual voices, and agent configuration but never mention custom voice cloning or consent workflows.

   ⚑ 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

   Deepgram Voice Agent—none0/10

   Evidence confirms TTS model selection exists (docs-10 references choosing a TTS model) but there is no documentation of a broad voice library or of plugging in multiple third-party TTS providers analogous to the multi-LLM-provider list (docs-9). Missing for 10: evidence of voice catalog/library breadth, evidence of multiple supported TTS providers/vendors, and any provider-switching mechanism for voice output.

   • [claimed-docs] “A multilingual voice agent has two model decisions: which STT model transcribes the user, and which TTS model speaks the agent.”

   proof ↗⚑ flag