Fin vs Sierra

Actions

1. developerThe agent takes real actions through my APIs — refunds, order changes, subscription updates — with scoped auth per action

   weight 3 · round to Fin
   Fin~partialclaimed6/10

   Fin's Procedures feature explicitly supports multi-step business logic with third-party systems (refunds, order changes, etc. are common examples in docs like 'Takes action to update external systems' and Procedures training), and data connectors (Stripe, Shopify) plus MCP/API access enable real API calls. However, evidence does not detail per-action scoped authentication/authorization — no documentation on granular auth scoping per action, permission boundaries, or audit trails for individual API calls. missing for 10: explicit scoped-auth-per-action documentation, permission/authorization model for individual actions, and independent verification that real refunds/subscription updates are executed successfully in production.

   • [claimed-docs] “Takes action to update external systems”
   • [claimed-docs] “Building Fin Procedures”
   • [claimed-docs] “Procedures let you train Fin to handle queries with multiple steps, business logic and third-party systems from start to finish.”
   • [claimed-docs] “Data connector templates are now generally available, with ready-to-install templates across Stripe, Shopify and Statuspage.”
   • [claimed-docs] “The Fin API platform includes APIs for answer generation and knowledge retrieval, available as a set or independently.”
   • [claimed-docs] “give you the tools to safely deploy AI within your own boundaries.”

   proof ↗⚑ flag

   Sierra~partialcommunity5/10

   Sierra's Agent SDK/Studio docs show agents can call customer APIs to execute actions (order changes, refunds implied by 'internal APIs' use), with guardrails, release governance, and human-in-the-loop approval, and a HN commenter confirms it wires directly into a customer's internal APIs. However there's no explicit documentation of per-action scoped auth/permissioning model for API calls. missing for 10: explicit scoped-auth/permission model per action, concrete refund/subscription action examples, independent confirmation of granular auth scoping.

   • [claimed-docs] “write customer journeys as code, track changes, and build sophisticated logic without giving up our development workflows”
   • [claimed-docs] “Understand and rapidly adjust agent behavior by inspecting API calls, logic traces, and more.”
   • [claimed-docs] “Agent Checks and Simulations catching problems proactively, merge approval workflows putting a person in the loop, and split traffic release…”
   • [community] “Having seen this set up for a customer, what is interesting is how it works with internal APIs for customers. It's all point and click in a …”

   proof ↗⚑ flag

Procedures

1. support ops leadI encode standard operating procedures the agent follows step-by-step for known issue types, with deterministic branching

   weight 2 · round drawn
   Fin✓fullclaimed8/10

   Fin has a dedicated "Building Fin Procedures" feature explicitly for encoding multi-step SOPs with business logic and branching, plus "Giving Fin Guidance" and escalation-rule configuration to control deterministic branching to humans or other steps, and a testing suite to validate procedures before launch. This directly matches the story of encoding SOPs with deterministic branching for known issue types. Missing for 10: independent hands-on verification of branching logic depth/determinism and more detail on how conditional branches are authored (docs are descriptive marketing/help-article summaries rather than technical specs).

   • [claimed-docs] “Building Fin Procedures”
   • [claimed-docs] “Procedures let you train Fin to handle queries with multiple steps, business logic and third-party systems from start to finish.”
   • [claimed-docs] “Giving Fin Guidance”
   • [claimed-docs] “Manage Fin AI Agent's escalation guidance and rules”
   • [claimed-docs] “you test it across thousands of scenarios before anything goes live, roll out changes with control, and evaluate every live conversation”
   • [claimed-docs] “Train Fin on your Procedures, knowledge, and policies, test performance before launch, deploy across every channel, then analyze and improve…”

   proof ↗⚑ flag

   Sierra✓fullcommunity8/10

   Sierra's Agent SDK/Studio explicitly supports authoring step-by-step journeys ('workflows') as code or no-code, with branching logic ('sophisticated logic'), SOP ingestion via Ghostwriter, and simulation/testing to verify deterministic behavior across scenarios. This directly matches encoding SOPs with deterministic branching for known issue types. missing for 10: independent/hands-on verification of deterministic branching behavior specifically (community evidence is generic, not focused on SOP branching), and docs are login-gated so full workflow-editor detail isn't independently viewable.

   • [claimed-docs] “write customer journeys as code, track changes, and build sophisticated logic without giving up our development workflows”
   • [claimed-docs] “Define step-by-step workflows for your agent from scratch, or use AI to instantly generate journeys from your existing operating procedures.”
   • [claimed-docs] “Upload SOPs, raw transcripts, or even audio interviews of subject matter experts to create customer journeys from scratch.”
   • [claimed-docs] “Verify your agent performs as expected across a wide range of scenarios and avoid regressions.”
   • [community] “Having seen this set up for a customer, what is interesting is how it works with internal APIs for customers. It's all point and click in a …”

   proof ↗⚑ flag

Agent access

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

   weight 2 · round to Fin
   Fin✓fullprobed8/10

   A live probe confirms Fin publishes an llms.txt file at https://fin.ai/llms.txt (HTTP 200) explicitly framed to help LLMs understand its content structure, and Fin also offers an MCP server and a CLI agents can be pointed at for setup. Missing for 10: markdown-doc mirrors (docs-md probe 404) and a discoverable OpenAPI spec (all candidates 404), which would round out agent-friendly documentation.

   • [probe] “PROBE llms.txt: HTTP 200 at https://fin.ai/llms.txt # Fin — llms.txt > This file helps large language models (LLMs) understand the content …”
   • [claimed-docs] “Point your AI coding Agent at the Fin CLI to set up Fin on your website and configure your Intercom helpdesk — all without leaving your codi…”
   • [probe] “official MCP server documented at https://developers.intercom.com/docs/guides/mcp”
   • [probe] “PROBE docs-md: HTTP 404 at https://fin.ai/help/en/.md”
   • [probe] “PROBE openapi: all candidate paths 404 (https://fin.ai/openapi.json, https://fin.ai/swagger.json, https://fin.ai/api/openapi.json, https://f…”

   proof ↗⚑ flag

   Sierra—none0/10

   Direct probes show no llms.txt at sierra.ai (404) and docs.sierra.ai/llms.txt merely resolves to the login SPA HTML shell rather than an actual plain-text agent-oriented index; the real docs are login-gated to contracted customers, so an agent cannot be pointed at a genuine llms.txt or open agent-oriented docs.

   • [probe] “PROBE llms.txt: HTTP 404 at https://sierra.ai/llms.txt”
   • [probe] “PROBE runtime finding (recorded 2026-09-10): docs.sierra.ai has no agent-legible surface — /llms.txt resolves (HTTP 200) to the login SPA's …”

   proof ↗⚑ flag

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

   weight 2 · round to Fin
   Fin~partialclaimed6/10

   Fin exposes a programmatic Agent API and Node/TypeScript SDK (intercom-fin-docs-3, intercom-fin-gh-3) that allows calling Fin from external code/services, which supports headless/CI-style automation, and webhooks (intercom-fin-docs-6) enable event-driven automation without a UI. However, there is no documented CI-specific tooling (e.g., a CLI test runner, GitHub Action, or exit-code-based automation harness) and the orchestration endpoints needed for autonomous agent-style headless runs are only in a Preview API (intercom-fin-docs-4). missing for 10: dedicated CI/CD integration or GitHub Action, documented headless test/run mode with exit codes, independent confirmation of automation reliability outside preview APIs.

   • [claimed-docs] “Fin can be accessed programmatically via an API. You call Fin from your o”
   • [claimed-docs] “The new orchestration endpoints — Discover capabilities, Ask Fin, Run a procedure, and Escalate to a human — are currently available in the …”
   • [claimed-docs] “Webhooks allow you to subscribe to real-time notifications of events happening in Intercom; a Contact created, an incoming Conversation rece…”
   • [github] “The Intercom TypeScript library provides convenient access to the Intercom APIs from TypeScript.”

   proof ↗⚑ flag

   Sierra~partialprobed4/10

   Sierra's Agent SDK is described as code-based with dev workflows retained, and one blog post explicitly notes publishing to ChatGPT can be done 'via CI/CD', implying some automation/pipeline support. However there is no dedicated CLI, headless runtime docs, or CI-specific guidance, and docs/API references are login-gated (probe shows /llms.txt is a login SPA and no public OpenAPI spec), so full headless/CI operation is unconfirmed. Missing for 10: explicit CLI/headless execution docs, public API/OpenAPI spec, independent CI usage reports.

   • [claimed-docs] “write customer journeys as code, track changes, and build sophisticated logic without giving up our development workflows”
   • [claimed-docs] “Publish to ChatGPT with one click: Make your agent available on ChatGPT with the push of a button, or via CI/CD.”
   • [probe] “PROBE llms.txt: HTTP 404 at https://sierra.ai/llms.txt”
   • [probe] “PROBE openapi: all candidate paths 404 (https://sierra.ai/openapi.json, https://sierra.ai/swagger.json, https://sierra.ai/api/openapi.json, …”
   • [probe] “PROBE runtime finding (recorded 2026-09-10): docs.sierra.ai has no agent-legible surface — /llms.txt resolves (HTTP 200) to the login SPA's …”

   proof ↗⚑ flag

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

   weight 3 · round to Fin
   Fin✓fullclaimed7/10

   Fin's own docs state it 'works through MCP or API Data Connectors for your business tools' (docs-40/54) and explicitly describe connecting the 'Zapier MCP connector to Fin' (intdir-3), confirming Fin can consume external MCP servers as tools. Missing for 10: independent/hands-on verification of MCP tool usage in production and a broader list of supported MCP servers beyond the Zapier example.

   • [claimed-docs] “It works through MCP or API Data Connectors for your business tools, integrations with Intercom or your existing helpdesk”
   • [claimed-docs] “It works through MCP or API Data Connectors for your business tools, integrations with Intercom or your existing helpdesk, and native suppor…”
   • [claimed-docs] “Connect the Zapier MCP connector to Fin Manage workspace owners”
   • [claimed-docs] “Learn how to use the Model Context Protocol (MCP) to enable AI agents to securely access and interact with your Intercom data whenever helpf…”
   • [claimed-docs] “MCP is a protocol that enables AI tools and applications to securely access and interact with your Intercom data whenever helpful.”

   proof ↗⚑ flag

   Sierra—none0/10

   No evidence anywhere in the pack mentions MCP servers or the ability to plug external MCP tool servers into Sierra's agents; Sierra's integration mentions are about internal APIs and custom systems, not MCP. missing for 10: any mention of MCP protocol support, MCP client configuration, or third-party tool server integration via MCP.

   ⚑ flag

4. ai-native userUse an official CLI

   weight 2 · round drawn
   Fin—none0/10

   Evidence shows Fin has an API, Node SDK, and MCP server, but no official CLI tool is documented or referenced anywhere in the evidence pack.

   ⚑ flag

   Sierra—none0/10

   No evidence of an official Sierra CLI tool; the Agent SDK mentions a code-based development workflow but nothing describing a CLI, and probes for llms.txt/openapi return 404s with no CLI reference anywhere in the pack.

   • [claimed-docs] “write customer journeys as code, track changes, and build sophisticated logic without giving up our development workflows”
   • [probe] “PROBE llms.txt: HTTP 404 at https://sierra.ai/llms.txt”
   • [probe] “PROBE openapi: all candidate paths 404 (https://sierra.ai/openapi.json, https://sierra.ai/swagger.json, https://sierra.ai/api/openapi.json, …”

   proof ↗⚑ flag

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

   weight 3 · round to Fin
   Fin~partialprobed7/10

   Fin ships a documented Fin Agent API and an official TypeScript SDK (intercom-node) with concrete usage examples, plus a separate API platform for answer generation/knowledge retrieval, giving AI-native users a real programmatic path to drive Fin. However, key orchestration endpoints (Discover capabilities, Ask Fin, Run a procedure, Escalate) are only in Preview API version, and automated probes found no discoverable OpenAPI/swagger spec, suggesting the public API surface is not fully standardized/discoverable yet. Missing for 10: stable (non-preview) orchestration endpoints, a machine-readable OpenAPI spec, and independent hands-on developer corroboration beyond vendor docs/SDK repo.

   • [claimed-docs] “Fin can be accessed programmatically via an API. You call Fin from your o”
   • [claimed-docs] “The new orchestration endpoints — Discover capabilities, Ask Fin, Run a procedure, and Escalate to a human — are currently available in the …”
   • [claimed-docs] “The Fin API platform includes APIs for answer generation and knowledge retrieval, available as a set or independently.”
   • [claimed-docs] “Build world-class customer experiences, agents, and products with the best-performing models for customer service.”
   • [github] “await client.aiContent.createContentImportSource({ url: "https://www.example.com" });”
   • [github] “List endpoints are paginated. The SDK provides an iterator so that you can simply loop over the items”
   • [github] “The Intercom TypeScript library provides convenient access to the Intercom APIs from TypeScript.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://fin.ai/openapi.json, https://fin.ai/swagger.json, https://fin.ai/api/openapi.json, https://f…”

   proof ↗⚑ flag

   Sierra~partialprobed4/10

   Sierra markets an Agent SDK for building 'customer journeys as code' with API-call inspection, implying a programmatic interface exists, but there's no publicly discoverable OpenAPI spec, llms.txt, or open API reference — docs.sierra.ai is login-gated to contracted customers rather than a documented public API. Missing for 10: publicly accessible API reference/OpenAPI spec, evidence of self-serve API keys or open documentation, independent developer confirmation of using the API without a sales contract.

   • [claimed-docs] “write customer journeys as code, track changes, and build sophisticated logic without giving up our development workflows”
   • [claimed-docs] “Understand and rapidly adjust agent behavior by inspecting API calls, logic traces, and more.”
   • [probe] “PROBE llms.txt: HTTP 404 at https://sierra.ai/llms.txt”
   • [probe] “PROBE openapi: all candidate paths 404 (https://sierra.ai/openapi.json, https://sierra.ai/swagger.json, https://sierra.ai/api/openapi.json, …”
   • [probe] “PROBE runtime finding (recorded 2026-09-10): docs.sierra.ai has no agent-legible surface — /llms.txt resolves (HTTP 200) to the login SPA's …”

   proof ↗⚑ flag

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

   weight 2 · round drawn
   Fin—none0/10

   Evidence covers API access, MCP, webhooks, and integrations, but there is no mention of scoped or least-privilege API credential/token issuance for agents (e.g., granular permission scopes, per-agent API keys). Absence of evidence for an applicable capability yields 'none'. missing for 10: scoped/least-privilege credential issuance, API key/token permission granularity, agent-specific credential management docs.

   • [claimed-docs] “Fin can be accessed programmatically via an API. You call Fin from your o”
   • [claimed-docs] “Learn how to use the Model Context Protocol (MCP) to enable AI agents to securely access and interact with your Intercom data whenever helpf…”
   • [claimed-docs] “MCP is a protocol that enables AI tools and applications to securely access and interact with your Intercom data whenever helpful.”

   proof ↗⚑ flag

   Sierra—none0/10

   No evidence anywhere in the pack of scoped or least-privilege API credential/token issuance for agents; docs describe agent building, workflows, channels, and analytics but nothing about credential scoping, permissions, or API key management. Probes even show no public OpenAPI/API docs are accessible (sierra-probe-2, sierra-probe-rt-3), reinforcing the absence of evidence.

   • [probe] “PROBE openapi: all candidate paths 404 (https://sierra.ai/openapi.json, https://sierra.ai/swagger.json, https://sierra.ai/api/openapi.json, …”
   • [probe] “PROBE runtime finding (recorded 2026-09-10): docs.sierra.ai has no agent-legible surface — /llms.txt resolves (HTTP 200) to the login SPA's …”

   proof ↗⚑ flag

7. ai-native userBuild against official SDKs

   weight 2 · round to Fin
   Fin~partialprobed6/10

   Intercom publishes an official TypeScript/Node SDK (intercom-node) with convenient API access and iterator support, plus a documented Fin Agent API and API platform for programmatic use. However, only one official SDK language is evidenced, there's no public OpenAPI spec (probe returned 404s across all candidate paths), and no independent corroboration of SDK quality/adoption exists. Missing for 10: multi-language SDK coverage, publicly discoverable OpenAPI/schema, and third-party validation of SDK reliability.

   • [github] “await client.aiContent.createContentImportSource({ url: "https://www.example.com" });”
   • [github] “List endpoints are paginated. The SDK provides an iterator so that you can simply loop over the items”
   • [github] “The Intercom TypeScript library provides convenient access to the Intercom APIs from TypeScript.”
   • [claimed-docs] “Fin can be accessed programmatically via an API. You call Fin from your o”
   • [claimed-docs] “The Fin API platform includes APIs for answer generation and knowledge retrieval, available as a set or independently.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://fin.ai/openapi.json, https://fin.ai/swagger.json, https://fin.ai/api/openapi.json, https://f…”

   proof ↗⚑ flag

   Sierra~partialprobed5/10

   Sierra advertises an official "Agent SDK" for writing customer journeys as code with logic traces and testing (sierra-docs-1/2/4), but runtime probes show no public OpenAPI/swagger spec and the SDK/docs reference is login-gated to contracted customers rather than openly available to any AI-native developer (sierra-probe-2, sierra-probe-rt-3). Missing for 10: publicly accessible API reference/OpenAPI spec, evidence of open sign-up or trial SDK access, and independent developer accounts of building against it outside a paid contract.

   • [claimed-docs] “write customer journeys as code, track changes, and build sophisticated logic without giving up our development workflows”
   • [claimed-docs] “Understand and rapidly adjust agent behavior by inspecting API calls, logic traces, and more.”
   • [claimed-docs] “Verify your agent performs as expected across a wide range of scenarios and avoid regressions.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://sierra.ai/openapi.json, https://sierra.ai/swagger.json, https://sierra.ai/api/openapi.json, …”
   • [probe] “PROBE runtime finding (recorded 2026-09-10): docs.sierra.ai has no agent-legible surface — /llms.txt resolves (HTTP 200) to the login SPA's …”

   proof ↗⚑ flag

8. ai-native userSubscribe to events via webhooks

   weight 2 · round to Fin
   Fin✓fullclaimed8/10

   Intercom's developer docs explicitly document webhooks for subscribing to real-time event notifications (Contact created, Conversation received, Outbound Message receipt), directly matching the story. Missing for 10: independent/hands-on corroboration of webhook reliability and broader event coverage beyond the three examples cited.

   • [claimed-docs] “Webhooks allow you to subscribe to real-time notifications of events happening in Intercom; a Contact created, an incoming Conversation rece…”
   • [claimed-docs] “Webhooks allow you to subscribe to real-time notifications of events happening in Intercom”

   proof ↗⚑ flag

   Sierra—none0/10

   No evidence in the pack mentions webhooks or event subscription capabilities for Sierra; docs pages describe SDK, studio, insights, voice, and channels features but nothing about outbound event notifications. Probes further show no public API spec or open documentation to confirm such a mechanism exists.

   • [probe] “PROBE openapi: all candidate paths 404 (https://sierra.ai/openapi.json, https://sierra.ai/swagger.json, https://sierra.ai/api/openapi.json, …”
   • [probe] “PROBE runtime finding (recorded 2026-09-10): docs.sierra.ai has no agent-legible surface — /llms.txt resolves (HTTP 200) to the login SPA's …”

   proof ↗⚑ flag

Agentic features

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

   weight 2 · round to Sierra
   Fin~partialclaimed6/10

   Fin's 'Insights', 'AI Recommendations', 'AI Topics', 'Trends', and CX Score features (docs-16, docs-47, docs-61) show it surfaces AI-generated analysis of conversation data, and 'Monitors'/'Custom AI Scorecards' extend this to proactive alerting. However this is framed around support-conversation analytics rather than a general 'insights from your data' experience, and there's no independent/hands-on validation of accuracy or usefulness of these AI-generated insights. missing for 10: independent corroboration of insight quality, broader data-source coverage beyond support conversations, concrete UI examples of AI-generated suggestions.

   • [claimed-docs] “Operator, CX Score, AI Topics, Trends, AI Recommendations, Monitors, Custom AI Scorecards, Incident Detection”
   • [claimed-docs] “Insights continuously analyzes every Fin and human conv”
   • [claimed-docs] “With Insights, Monitors New, and Recommendations, you have everything you need to understand, measure, and improve your customer experience”
   • [claimed-docs] “Train Fin on your Procedures, knowledge, and policies, test performance before launch, deploy across every channel, then analyze and improve…”

   proof ↗⚑ flag

   Sierra✓fullcommunity8/10

   Sierra's Explorer and Insights products explicitly deliver AI-generated insights: natural-language querying across conversations, automatic weekly briefings on trends/issues with recommendations, and click-to-investigate drill-downs on report data, plus explainability of agent reasoning. This is a first-party documented feature set directly matching the story, with some independent corroboration of Sierra's data-driven production use (sierra-comm-2/3). Missing for 10: independent hands-on validation of the insights/briefing feature specifically (community evidence is about agent setup, not analytics quality), and no detail on data freshness/accuracy limits.

   • [claimed-docs] “Ask any question about your customer experience in natural language, and Explorer identifies the answer across thousands of real conversatio…”
   • [claimed-docs] “Explorer automatically delivers a weekly briefing on key trends, emerging issues, and recommendations — without you having to ask.”
   • [claimed-docs] “Click any data point on a report to launch Explorer and instantly investigate what's driving that trend.”
   • [claimed-docs] “Understand the reasoning behind every agent action or answer—view knowledge sources, systems accessed, and more.”
   • [community] “Having seen this set up for a customer, what is interesting is how it works with internal APIs for customers. It's all point and click in a …”
   • [community] “The magic isn't in a new LLM technology, it is in reliably productionizing a solution for real-world problems... fill in the gap of missing …”

   proof ↗⚑ flag

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

   weight 2 · round drawn
   Fin~partialcommunity6/10

   Fin's Procedures, Tasks, Workflows, and Proactive Outbound Suite are documented as letting Fin autonomously handle multi-step customer queries, business logic, and third-party system actions end-to-end without human intervention, and Fin resolves a majority of queries (76%) without agent involvement. However, evidence is entirely vendor marketing/help docs with no independent or hands-on confirmation of scheduling/background execution mechanics, and one community comment questions whether open-ended autonomous agent behavior is even desirable versus deterministic workflows. Missing for 10: independent verification of autonomous background execution, technical detail on triggers/scheduling for Procedures/Tasks, and hands-on confirmation that these run without ongoing human oversight.

   • [claimed-docs] “Building Fin Procedures”
   • [claimed-docs] “Manage your Fin workflows”
   • [claimed-docs] “Introduction to Fin Tasks”
   • [claimed-docs] “Procedures let you train Fin to handle queries with multiple steps, business logic and third-party systems from start to finish.”
   • [claimed-docs] “Proactive Outbound Suite”
   • [claimed-docs] “Fin resolves 76% of customer queries on average, and handles multi-step queries end to end. Your customers get fast, accurate answers. Your …”
   • [community] “A commenter with a decade of customer-service AI/NLP experience argued 99% of support tasks (order status, returns, troubleshooting) are ful…”

   proof ↗⚑ flag

   Sierra~partialcommunity6/10

   Sierra's agents are built to operate autonomously across channels (chat, phone, email, SMS) once deployed, and Explorer explicitly runs in the background to deliver automatic weekly briefings and trend detection 'without you having to ask,' which is genuine unprompted automation. However, there's no documentation of user-configurable scheduled tasks/triggers beyond the always-on conversational agent and the one named automatic-briefing feature. Missing for 10: explicit support for user-defined scheduled/triggered background jobs beyond conversation handling and briefings, and independent/hands-on confirmation of autonomous background execution reliability.

   • [claimed-docs] “Explorer automatically delivers a weekly briefing on key trends, emerging issues, and recommendations — without you having to ask.”
   • [claimed-docs] “Build once and deploy across any channel—chat, phone, email, SMS, and messaging.”
   • [claimed-docs] “Guide care reps through next steps—whether in chat or on calls—automatically capturing and updating customer context as work progresses.”
   • [community] “Having seen this set up for a customer, what is interesting is how it works with internal APIs for customers. It's all point and click in a …”

   proof ↗⚑ flag

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

   weight 3 · round to Fin
   Fin✓fullcommunity8/10

   Fin is itself a built-in AI agent that customers and support teams delegate tasks to — it resolves ~76% of queries end-to-end, handles multi-step 'Procedures' with business logic, takes actions on external systems, and works across channels (chat, email, voice, Slack, WhatsApp). This is documented extensively in first-party docs/help articles covering delegation-style use (Fin Tasks, Fin Procedures, escalation rules, API/Agent API for programmatic delegation). missing for 10: independent hands-on validation of delegation quality (one HN commenter says they've never seen Fin in the wild, and another questions whether autonomous agent reasoning is even the right approach vs deterministic workflows), and no live product screenshots/demo confirming smooth end-user delegation experience.

   • [claimed-docs] “Handles tickets, cases, emails, live chat, WhatsApp, SMS, and more”
   • [claimed-docs] “Takes action to update external systems”
   • [claimed-docs] “Building Fin Procedures”
   • [claimed-docs] “Introduction to Fin Tasks”
   • [claimed-docs] “Procedures let you train Fin to handle queries with multiple steps, business logic and third-party systems from start to finish.”
   • [claimed-docs] “Fin resolves 76% of customer queries on average, and handles multi-step queries end to end. Your customers get fast, accurate answers. Your …”
   • [claimed-docs] “Fin can be accessed programmatically via an API. You call Fin from your o”
   • [community] “"I've never seen or interacted with Fin that I've noticed... I suspect it's very visible for Intercom, but not necessarily so for everyone e…”
   • [community] “A commenter with a decade of customer-service AI/NLP experience argued 99% of support tasks (order status, returns, troubleshooting) are ful…”

   proof ↗⚑ flag

   Sierra~partialclaimed6/10

   Sierra's Ghostwriter feature lets users delegate agent-building tasks to a built-in AI assistant via natural-language prompts (build/modify agents, generate journeys from SOPs/transcripts), fitting the 'delegate tasks to built-in AI assistant' story. However, this is vendor-only documentation with no independent/hands-on corroboration of Ghostwriter specifically; community evidence instead describes manual point-and-click agent setup, not AI-assistant delegation. Missing for 10: independent/hands-on validation of Ghostwriter's delegation capability, and detail on scope/limits of what can be delegated.

   • [claimed-docs] “Build or modify agents by describing how you want them to behave. Update workflows, systems integrations, guardrails, tone, and style with s…”
   • [claimed-docs] “Upload SOPs, raw transcripts, or even audio interviews of subject matter experts to create customer journeys from scratch.”
   • [claimed-docs] “Define step-by-step workflows for your agent from scratch, or use AI to instantly generate journeys from your existing operating procedures.”

   proof ↗⚑ flag

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

   weight 2 · round to Sierra
   Fin~partialclaimed6/10

   Fin's core function is responding to customer queries in natural language across channels, and admins can 'give Fin guidance' and train Procedures using natural-language instructions (docs-22, docs-44, docs-60); the Fin CLI also lets an AI coding agent configure Fin setup based on natural-language prompts (docs-38). However there's no clear evidence of a full natural-language command interface for operating/administering the product itself (e.g., configuring settings, running reports) beyond training/guidance content. Missing for 10: documented NL-driven admin console commands, evidence of broad NL task execution beyond content/guidance training, and independent verification of this capability in practice.

   • [claimed-docs] “Giving Fin Guidance”
   • [claimed-docs] “Procedures let you train Fin to handle queries with multiple steps, business logic and third-party systems from start to finish.”
   • [claimed-docs] “Point your AI coding Agent at the Fin CLI to set up Fin on your website and configure your Intercom helpdesk — all without leaving your codi…”
   • [claimed-docs] “Train Fin on your knowledge, data, policies, and tone of voice. Fin will use everything it knows to deliver consistent, accurate answers”

   proof ↗⚑ flag

   Sierra✓fullcommunity7/10

   Sierra provides explicit natural-language control surfaces: Ghostwriter lets users 'build or modify agents by describing how you want them to behave' with 'simple prompts' for workflows, integrations, guardrails, tone and style, and Explorer lets users 'ask any question about your customer experience in natural language.' This directly matches an AI-native user operating the product via NL commands rather than only clicking through UI. Missing for 10: independent hands-on corroboration of NL command reliability/scope beyond vendor docs, and no evidence of NL control over the entire platform (e.g., release governance, channel deployment) rather than just Ghostwriter/Explorer.

   • [claimed-docs] “Build or modify agents by describing how you want them to behave. Update workflows, systems integrations, guardrails, tone, and style with s…”
   • [claimed-docs] “Upload SOPs, raw transcripts, or even audio interviews of subject matter experts to create customer journeys from scratch.”
   • [claimed-docs] “Ask any question about your customer experience in natural language, and Explorer identifies the answer across thousands of real conversatio…”
   • [community] “Having seen this set up for a customer, what is interesting is how it works with internal APIs for customers. It's all point and click in a …”

   proof ↗⚑ flag

Api quality

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

   weight 2 · round drawn
   Fin—none0/10

   While Fin has API docs (developers.intercom.com) and an SDK on GitHub, there is no evidence of an interactive API reference with runnable/try-it examples; a probe explicitly found no OpenAPI/Swagger spec published at any candidate path, and no docs mention a live API console.

   • [claimed-docs] “Fin can be accessed programmatically via an API. You call Fin from your o”
   • [probe] “PROBE openapi: all candidate paths 404 (https://fin.ai/openapi.json, https://fin.ai/swagger.json, https://fin.ai/api/openapi.json, https://f…”
   • [github] “The Intercom TypeScript library provides convenient access to the Intercom APIs from TypeScript.”

   proof ↗⚑ flag

   Sierra—none0/10

   Probes show no public OpenAPI/interactive reference (404s at openapi.json paths, llms.txt returns login SPA shell), and docs are login-gated to contracted customers rather than an open interactive API reference with runnable examples.

   • [probe] “PROBE llms.txt: HTTP 404 at https://sierra.ai/llms.txt”
   • [probe] “PROBE openapi: all candidate paths 404 (https://sierra.ai/openapi.json, https://sierra.ai/swagger.json, https://sierra.ai/api/openapi.json, …”
   • [probe] “PROBE runtime finding (recorded 2026-09-10): docs.sierra.ai has no agent-legible surface — /llms.txt resolves (HTTP 200) to the login SPA's …”

   proof ↗⚑ flag

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

   weight 2 · round drawn
   Fin—none0/10

   Fin has documented REST APIs (Fin Agent API, Node/TS SDK) but no evidence of a downloadable machine-readable spec; a direct probe for OpenAPI/swagger files at common paths returned 404 for all candidates, and no docs page links such a spec.

   • [probe] “PROBE openapi: all candidate paths 404 (https://fin.ai/openapi.json, https://fin.ai/swagger.json, https://fin.ai/api/openapi.json, https://f…”
   • [claimed-docs] “Fin can be accessed programmatically via an API. You call Fin from your o”
   • [claimed-docs] “The new orchestration endpoints — Discover capabilities, Ask Fin, Run a procedure, and Escalate to a human — are currently available in the …”

   proof ↗⚑ flag

   Sierra—none0/10

   Direct probes show no OpenAPI/Swagger spec at common paths (all 404) and no machine-readable llms.txt index; docs are login-gated rather than publicly exposing a spec. missing for 10: publicly downloadable OpenAPI/Swagger file, any machine-readable API spec endpoint.

   • [probe] “PROBE llms.txt: HTTP 404 at https://sierra.ai/llms.txt”
   • [probe] “PROBE openapi: all candidate paths 404 (https://sierra.ai/openapi.json, https://sierra.ai/swagger.json, https://sierra.ai/api/openapi.json, …”
   • [probe] “PROBE runtime finding (recorded 2026-09-10): docs.sierra.ai has no agent-legible surface — /llms.txt resolves (HTTP 200) to the login SPA's …”

   proof ↗⚑ flag

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

   weight 1 · round to Sierra
   Fin~partialclaimed4/10

   Fin documents a testing/preview workflow ('How to preview and test Fin', testing across thousands of scenarios before going live) that implies an isolated test/preview mode, but no evidence explicitly describes a sandbox environment separate from production data or confirms production data isolation during testing. Missing for 10: explicit sandbox/staging environment documentation, confirmation that test scenarios don't touch live customer data, and any independent/hands-on corroboration of this isolation.

   • [claimed-docs] “How to preview and test Fin”
   • [claimed-docs] “you test it across thousands of scenarios before anything goes live, roll out changes with control, and evaluate every live conversation”
   • [claimed-docs] “you test it across thousands of scenarios before anything goes live, roll out changes with control, and evaluate every live conversation... …”

   proof ↗⚑ flag

   Sierra~partialclaimed5/10

   Sierra's docs mention scenario testing and regression avoidance (sierra-docs-4) and 'Agent Checks and Simulations' for proactive problem catching (sierra-docs-18), implying some pre-production testing capability, but there is no explicit mention of a dedicated sandbox environment isolated from production data. Missing for 10: explicit sandbox/staging environment documentation, confirmation that test runs don't touch production data, and independent/hands-on verification of this separation.

   • [claimed-docs] “Verify your agent performs as expected across a wide range of scenarios and avoid regressions.”
   • [claimed-docs] “Agent Checks and Simulations catching problems proactively, merge approval workflows putting a person in the loop, and split traffic release…”

   proof ↗⚑ flag

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

   weight 2 · round drawn
   Fin—none0/10

   The evidence shows Fin has a REST API with a 'Preview API version' for new endpoints (docs-4) and a general changes/changelog page (docs-1), but there is no documented versioning scheme or explicit deprecation policy (e.g., version sunset timelines, backward-compatibility guarantees) cited anywhere in the pack.

   • [claimed-docs] “Fin can be accessed programmatically via an API. You call Fin from your o”
   • [claimed-docs] “The new orchestration endpoints — Discover capabilities, Ask Fin, Run a procedure, and Escalate to a human — are currently available in the …”
   • [claimed-docs] “See exactly when errors started, which status codes came back, and confirm a fix actually worked.”

   proof ↗⚑ flag

   Sierra—none0/10

   No evidence of a public versioned API reference or a documented deprecation policy; probes show no OpenAPI spec and docs are login-gated, and llms.txt/openapi.json all 404 or resolve to a login shell rather than API docs.

   • [probe] “PROBE llms.txt: HTTP 404 at https://sierra.ai/llms.txt”
   • [probe] “PROBE openapi: all candidate paths 404 (https://sierra.ai/openapi.json, https://sierra.ai/swagger.json, https://sierra.ai/api/openapi.json, …”
   • [probe] “PROBE runtime finding (recorded 2026-09-10): docs.sierra.ai has no agent-legible surface — /llms.txt resolves (HTTP 200) to the login SPA's …”

   proof ↗⚑ flag

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

   weight 2 · round to Fin
   Fin~partialclaimed3/10

   Fin exposes a programmatic API and TypeScript SDK with a pagination iterator for looping over items, plus a content-import endpoint, implying some capacity for scripted bulk actions, but there is no documented bulk-edit, bulk-resolve, or batch-action feature for AI-native users. missing for 10: explicit bulk operation/batch endpoint documentation, hands-on evidence of processing many items in one call, and any UI/CLI bulk-action support.

   • [claimed-docs] “Fin can be accessed programmatically via an API. You call Fin from your o”
   • [github] “await client.aiContent.createContentImportSource({ url: "https://www.example.com" });”
   • [github] “List endpoints are paginated. The SDK provides an iterator so that you can simply loop over the items”

   proof ↗⚑ flag

   Sierra—none0/10

   No evidence describes bulk operations across many items (e.g., batch editing knowledge entries, mass workflow updates, or bulk conversation actions). The docs describe individual agent building, knowledge editing, and analytics tools, but nothing about performing actions at scale across many items simultaneously.

   ⚑ flag

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

   weight 3 · round to Fin
   Fin~partialclaimed6/10

   Fin exposes several automation primitives that fit an event→action rule model: webhooks that fire on real-time Intercom events, configurable escalation guidance/rules, Fin Procedures/workflows, and Operator/Monitors for incident-triggered behavior. This is more than none, but the pack lacks a first-class 'if event X then action Y' rule-builder walkthrough or independent confirmation of how flexible/robust these triggers are. Missing for 10: a documented dedicated rules/automation builder UI, concrete example of a user-defined trigger-condition-action rule, and independent (non-vendor) evidence that automation rules work reliably in practice.

   • [claimed-docs] “Webhooks allow you to subscribe to real-time notifications of events happening in Intercom; a Contact created, an incoming Conversation rece…”
   • [claimed-docs] “Manage Fin AI Agent's escalation guidance and rules”
   • [claimed-docs] “Building Fin Procedures”
   • [claimed-docs] “Manage your Fin workflows”
   • [claimed-docs] “Operator, CX Score, AI Topics, Trends, AI Recommendations, Monitors, Custom AI Scorecards, Incident Detection”

   proof ↗⚑ flag

   Sierra~partialclaimed5/10

   Sierra supports building workflows/journeys and guardrail-based release governance (e.g., agent checks, split traffic, merge approval) which implies some rule-based triggering, and Ghostwriter/Agent Studio let users define step-by-step logic, but there's no explicit documentation of an event-driven 'if X happens, trigger Y automatically' rules engine for AI-native users to configure independently. missing for 10: explicit event-trigger/rules-engine documentation, API/SDK examples of automated action-on-event configuration, independent verification of this specific automation capability.

   • [claimed-docs] “Define step-by-step workflows for your agent from scratch, or use AI to instantly generate journeys from your existing operating procedures.”
   • [claimed-docs] “Agent Checks and Simulations catching problems proactively, merge approval workflows putting a person in the loop, and split traffic release…”
   • [claimed-docs] “Initiate workflows directly from a conversation—no tab-switching, no referencing instructions, no lost context.”

   proof ↗⚑ flag

3. ai-native userSchedule recurring jobs or workflows

   weight 2 · round drawn
   Fin—none0/10

   Fin's evidence covers webhooks (event-driven, not scheduled), procedures, and a Proactive Outbound Suite, but nothing describes cron-like scheduling of recurring jobs or workflows on a time-based schedule. missing for 10: any documentation of scheduled/recurring workflow triggers, cron-style job scheduling, or recurring automation configuration.

   • [claimed-docs] “Webhooks allow you to subscribe to real-time notifications of events happening in Intercom; a Contact created, an incoming Conversation rece…”
   • [claimed-docs] “Proactive Outbound Suite”
   • [claimed-docs] “Procedures let you train Fin to handle queries with multiple steps, business logic and third-party systems from start to finish.”

   proof ↗⚑ flag

   Sierra—none0/10

   Sierra's evidence covers agent building, workflows, channels, analytics, and release governance, but nothing describes scheduling recurring jobs/workflows (e.g., cron-like triggers or timed automation runs) for AI-native users. Missing for 10: any documentation of scheduled/recurring job execution, trigger-based automation, or timer-based workflow runs.

   ⚑ flag

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

   weight 1 · round to Sierra
   Fin—none0/10

   The evidence pack shows Fin Procedures/workflow management and testing before go-live ('test it across thousands of scenarios before anything goes live, roll out changes with control'), but there is no concrete documentation of version history, diffing/review of automation changes, or an explicit rollback mechanism for Procedures/Workflows/Guidance. Nothing describes a changelog, version comparison, or a 'revert to previous version' feature for automations.

   • [claimed-docs] “Building Fin Procedures”
   • [claimed-docs] “Manage your Fin workflows”
   • [claimed-docs] “you test it across thousands of scenarios before anything goes live, roll out changes with control, and evaluate every live conversation”

   proof ↗⚑ flag

   Sierra~partialprobed5/10

   Sierra's Agent SDK is described as 'journeys as code' with change tracking, and release-governance docs mention merge approval, agent checks/simulations, and split-traffic gradual rollouts — pointing to versioned, reviewable release workflows. However, no explicit rollback/revert mechanism is documented, and docs are login-gated so no independent confirmation exists. missing for 10: explicit rollback capability, independent/hands-on confirmation of version history and revert function.

   • [claimed-docs] “write customer journeys as code, track changes, and build sophisticated logic without giving up our development workflows”
   • [claimed-docs] “Agent Checks and Simulations catching problems proactively, merge approval workflows putting a person in the loop, and split traffic release…”
   • [probe] “PROBE runtime finding (recorded 2026-09-10): docs.sierra.ai has no agent-legible surface — /llms.txt resolves (HTTP 200) to the login SPA's …”

   proof ↗⚑ flag

Channels

1. support leaderOne agent covers chat, email, and in-app, plus the channels my customers actually use — Slack, WhatsApp, social

   weight 2 · round to Fin
   Fin✓fullclaimed7/10

   Fin explicitly documents multi-channel support spanning live chat/messenger (in-app), email, Slack, WhatsApp, SMS, and voice, with dedicated channel pages for each (docs-9, docs-50, docs-51, docs-65, docs-48). Missing for 10: explicit named coverage of broader 'social' channels like Instagram/Facebook/X/Twitter — only 'and more' is implied, not documented by name, so the specific 'social' claim in the story is only partially evidenced.

   • [claimed-docs] “Handles tickets, cases, emails, live chat, WhatsApp, SMS, and more”
   • [claimed-docs] “Fin delivers unrivaled performance across every channel. Wherever customers reach out, Fin provides fast, accurate, personalized support”
   • [claimed-docs] “Fin over Email resolves everything from simple FAQs to complex threads with attachments and multiple recipients”
   • [claimed-docs] “Fin for Slack supports your customers directly in Slack communities and dedicated Connect channels, resolving questions instantly with repli…”
   • [claimed-docs] “Fin for messenger brings the highest-performing AI Agent for customer service to live chat, delivering exceptional, on-brand support”

   proof ↗⚑ flag

   Sierra~partialclaimed5/10

   Sierra's docs confirm one agent can be built once and deployed across chat, phone, email, SMS, and messaging channels (sierra-docs-3), with channel-specific tuning like Voice Personas (sierra-docs-16) and even ChatGPT publishing (sierra-docs-17), showing broad omnichannel intent. However, none of the evidence explicitly names Slack, WhatsApp, or social media as supported channels, leaving the specific channels support leaders care about unconfirmed. Missing for 10: explicit documentation or independent confirmation that Slack, WhatsApp, and social platforms are supported channels, plus hands-on evidence of a single agent operating consistently across these specific channels.

   • [claimed-docs] “Build once and deploy across any channel—chat, phone, email, SMS, and messaging.”
   • [claimed-docs] “Use Voice Personas to design how your agent sounds, speaks, and connects, powered by a constellation of models tuned across 59 languages.”
   • [claimed-docs] “Publish to ChatGPT with one click: Make your agent available on ChatGPT with the push of a button, or via CI/CD.”

   proof ↗⚑ flag

Languages

1. support leaderThe agent supports customers in many languages, even where my knowledge base exists only in English

   weight 2 · round to Fin
   Fin✓fullclaimed7/10

   Intercom/Fin explicitly documents multi-language support ('Use Fin AI Agent in multiple languages') allowing Fin to answer in customers' languages even when the knowledge base content is authored in English, which directly matches the story. Missing for 10: independent/hands-on verification of translation quality or explicit detail on how English-only knowledge base content is translated/handled per language.

   • [claimed-docs] “Use Fin AI Agent in multiple languages”

   proof ↗⚑ flag

   Sierra~partialclaimed6/10

   Sierra documents multilingual voice capability explicitly ('Voice Personas... tuned across 59 languages') and general multi-channel deployment, implying broad language coverage for customer-facing conversations. However, there is no direct evidence about handling a knowledge base that exists only in English—no mention of automatic translation/grounding of English-only content into other languages, so the specific claim in the story (KB gap bridging) is unevidenced. missing for 10: explicit documentation on cross-language grounding from English-only knowledge base, independent verification of multilingual quality beyond voice.

   • [claimed-docs] “Use Voice Personas to design how your agent sounds, speaks, and connects, powered by a constellation of models tuned across 59 languages.”
   • [claimed-docs] “Build once and deploy across any channel—chat, phone, email, SMS, and messaging.”

   proof ↗⚑ flag

Voice

1. support leaderThe agent handles phone calls — speech in, speech out — with the same knowledge and actions as chat

   weight 2 · round to Sierra
   Fin✓fullclaimed7/10

   Fin Voice is a documented dedicated product ('Deploy Fin Voice', 'Fin Voice 2 runs on Apex Flash... latency-sensitive tasks', 'grounded in your knowledge base, and trained to apply your specific policies on every call'), indicating speech-in/speech-out phone support using the same knowledge base as chat. Missing for 10: independent/hands-on validation of voice call quality or action-taking parity specifically for phone, and no detail on how actions (e.g., updating systems) work identically on voice vs chat.

   • [claimed-docs] “Deploy Fin Voice”
   • [claimed-docs] “grounded in your knowledge base, and trained to apply your specific policies on every call.”
   • [claimed-docs] “Fin Voice 2 runs on Apex Flash - our latest Fin model, built for latency-sensitive tasks.”
   • [claimed-docs] “Fin delivers unrivaled performance across every channel. Wherever customers reach out, Fin provides fast, accurate, personalized support”

   proof ↗⚑ flag

   Sierra✓fullclaimed8/10

   Sierra explicitly markets voice as a first-class channel with the same agent logic/knowledge as chat ('Build once and deploy across any channel—chat, phone, email, SMS'), plus voice-specific features like Voice Personas across 59 languages, replacing IVR with empathetic voice agents, and phone payments without IVR handoff. Missing for 10: independent/hands-on verification that voice calls actually share identical knowledge/actions with chat in production (only vendor docs, no community confirmation of phone-specific parity).

   • [claimed-docs] “Build once and deploy across any channel—chat, phone, email, SMS, and messaging.”
   • [claimed-docs] “Use Voice Personas to design how your agent sounds, speaks, and connects, powered by a constellation of models tuned across 59 languages.”
   • [claimed-docs] “Collect card and ACH payments entirely over the phone with no IVR handoff.”
   • [claimed-docs] “Replace rigid IVR menus with an empathetic voice agent that understands customer context”
   • [claimed-docs] “Guide care reps through next steps—whether in chat or on calls—automatically capturing and updating customer context as work progresses.”

   proof ↗⚑ flag

Handoff

1. support leaderWhen the agent escalates, the human gets the full conversation, a summary, and collected details — the customer never repeats themselves

   weight 3 · round drawn
   Fin~partialclaimed6/10

   Docs confirm Fin can transfer/escalate to human agents within the preferred inbox, has configurable escalation guidance/rules, and supports hand-off via JavaScript and an orchestration 'Escalate to a human' API — showing escalation handoff is a real, built feature. However, none of the evidence explicitly confirms that a generated summary or a structured collection of gathered details is passed along with the full conversation to the human agent, so the 'never repeats themselves' guarantee is not directly documented. Missing for 10: explicit documentation of automatic conversation summary generation at escalation, evidence that collected customer details are packaged and handed off, and independent/hands-on confirmation the handoff actually eliminates repetition.

   • [claimed-docs] “The new orchestration endpoints — Discover capabilities, Ask Fin, Run a procedure, and Escalate to a human — are currently available in the …”
   • [claimed-docs] “Transfers to agents directly in preferred Inbox”
   • [claimed-docs] “Manage Fin AI Agent's escalation guidance and rules”
   • [claimed-docs] “Hand-off with JavaScript”

   proof ↗⚑ flag

   Sierra~partialclaimed6/10

   Sierra's Live Assist product explicitly targets escalation handoff, claiming reps are guided through next steps with automatically captured and updated customer context and can pick up conversations 'no tab-switching, no referencing instructions, no lost context' — directly addressing the no-repeat-yourself goal. However, there's no explicit mention of a generated conversation summary artifact or hands-on/independent confirmation that reps actually receive full transcript + summary + collected details in practice. Missing for 10: explicit summary-generation evidence, independent/hands-on validation of the handoff experience, confirmation reps see full conversation history alongside context.

   • [claimed-docs] “Guide care reps through next steps—whether in chat or on calls—automatically capturing and updating customer context as work progresses.”
   • [claimed-docs] “Initiate workflows directly from a conversation—no tab-switching, no referencing instructions, no lost context.”

   proof ↗⚑ flag

Rules

1. support ops leadI configure when the agent must hand off — by topic, sentiment, customer tier, or explicit request — and it reliably obeys

   weight 2 · round to Fin
   Fin~partialclaimed6/10

   Docs explicitly cover configuring escalation/handoff behavior — 'Manage Fin AI Agent's escalation guidance and rules', 'Giving Fin Guidance', 'Hand-off with JavaScript', an orchestration 'Escalate to a human' API endpoint, and 'Transfers to agents directly in preferred Inbox' — showing topic/guidance-based and explicit-request handoff configuration exists. However, none of the evidence specifically documents sentiment- or customer-tier-based handoff triggers, nor is there independent/hands-on verification that the agent reliably obeys these rules in practice. Missing for 10: explicit sentiment/tier-based escalation config docs, and third-party evidence confirming reliable adherence to configured handoff rules.

   • [claimed-docs] “Manage Fin AI Agent's escalation guidance and rules”
   • [claimed-docs] “Giving Fin Guidance”
   • [claimed-docs] “Hand-off with JavaScript”
   • [claimed-docs] “The new orchestration endpoints — Discover capabilities, Ask Fin, Run a procedure, and Escalate to a human — are currently available in the …”
   • [claimed-docs] “Transfers to agents directly in preferred Inbox”

   proof ↗⚑ flag

   Sierra~partialclaimed3/10

   Sierra's docs mention configurable guardrails, workflows, and a 'Live Assist' human-handoff product, implying some escalation mechanism exists, but no evidence describes explicit configuration of handoff triggers by topic, sentiment, customer tier, or explicit customer request, nor proof of reliable adherence to such rules. Missing for 10: documentation of specific trigger types (topic/sentiment/tier/request), configuration UI/API for these rules, and evidence (first-party or independent) that the agent reliably obeys them in production.

   • [claimed-docs] “Build or modify agents by describing how you want them to behave. Update workflows, systems integrations, guardrails, tone, and style with s…”
   • [claimed-docs] “Guide care reps through next steps—whether in chat or on calls—automatically capturing and updating customer context as work progresses.”
   • [claimed-docs] “Agent Checks and Simulations catching problems proactively, merge approval workflows putting a person in the loop, and split traffic release…”
   • [claimed-docs] “Define step-by-step workflows for your agent from scratch, or use AI to instantly generate journeys from your existing operating procedures.”

   proof ↗⚑ flag

Hallucination

1. ai-native userGuardrails stop the agent from inventing policies, prices, or promises — off-knowledge questions get a safe decline, not a guess

   weight 3 · round to Fin
   Fin~partialclaimed6/10

   Docs show Fin is 'trained on your knowledge, data, policies, and tone' and grounded in the knowledge base (docs-46/60/49), with configurable 'escalation guidance and rules' (docs-25) and pre-launch testing across scenarios (docs-45/59), which together imply guardrails against off-knowledge answers and hand-off rather than free invention. However there is no explicit documentation of a 'safe decline' behavior or hallucination-refusal mechanism, and no independent/hands-on evidence confirming Fin actually declines rather than guesses on off-knowledge questions. Missing for 10: explicit safe-decline/refusal documentation, third-party or hands-on verification that Fin avoids inventing policies/prices, and quantified guardrail testing results.

   • [claimed-docs] “Train Fin on your knowledge, data, policies, and tone of voice.”
   • [claimed-docs] “Train Fin on your knowledge, data, policies, and tone of voice. Fin will use everything it knows to deliver consistent, accurate answers”
   • [claimed-docs] “Manage Fin AI Agent's escalation guidance and rules”
   • [claimed-docs] “you test it across thousands of scenarios before anything goes live, roll out changes with control, and evaluate every live conversation”
   • [claimed-docs] “you test it across thousands of scenarios before anything goes live, roll out changes with control, and evaluate every live conversation... …”
   • [claimed-docs] “grounded in your knowledge base, and trained to apply your specific policies on every call.”
   • [claimed-docs] “give you the tools to safely deploy AI within your own boundaries.”
   • [claimed-docs] “We meet the world's leading compliance standards—and give you the tools to safely deploy AI within your own boundaries.”

   proof ↗⚑ flag

   Sierra~partialclaimed4/10

   Sierra's docs mention grounding agents in knowledge/FAQs/policies (sierra-docs-7) and general 'guardrails' as a configurable behavior via Ghostwriter (sierra-docs-8), plus release-governance guardrails like Agent Checks/Simulations (sierra-docs-18) and visibility into reasoning/knowledge sources (sierra-docs-13). However, no evidence explicitly describes a 'safe decline' mechanism for off-knowledge questions or confirms the agent won't invent prices/policies rather than guess. Missing for 10: explicit documentation or hands-on proof of decline-on-unknown behavior, third-party validation that hallucinated policies/prices are prevented, and detail on the unexplored 'trust-and-reliability' product page.

   • [claimed-docs] “View, manage, and edit knowledge data such as Help Center content, FAQs, and policies that ground your agent.”
   • [claimed-docs] “Build or modify agents by describing how you want them to behave. Update workflows, systems integrations, guardrails, tone, and style with s…”
   • [claimed-docs] “Understand the reasoning behind every agent action or answer—view knowledge sources, systems accessed, and more.”
   • [claimed-docs] “Agent Checks and Simulations catching problems proactively, merge approval workflows putting a person in the loop, and split traffic release…”

   proof ↗⚑ flag

Supervision

1. support ops leadLaunch in a supervised mode where the agent drafts replies for human approval before anything reaches a customer

   weight 2 · round drawn
   Fin—none0/10

   Evidence shows Fin's guardrails center on escalation rules, hand-off to human agents, and pre-launch testing/preview (docs-25, docs-29, docs-31, docs-45), but nothing describes a supervised/draft-for-approval mode where Fin composes replies that a human must approve before they reach the customer on every interaction. Community commentary (comm-3) even argues for more deterministic, human-controlled workflows, but that's not evidence Fin ships this specific approval-gate mode.

   • [claimed-docs] “Manage Fin AI Agent's escalation guidance and rules”
   • [claimed-docs] “How to preview and test Fin”
   • [claimed-docs] “Hand-off with JavaScript”
   • [claimed-docs] “you test it across thousands of scenarios before anything goes live, roll out changes with control, and evaluate every live conversation”
   • [community] “A commenter with a decade of customer-service AI/NLP experience argued 99% of support tasks (order status, returns, troubleshooting) are ful…”

   proof ↗⚑ flag

   Sierra—none0/10

   Sierra's evidence covers release governance (merge-approval for deploying agent changes) and Live Assist (guiding human reps in real time), but neither describes a mode where the agent drafts individual customer replies that a human must approve before they are sent. No docs or community evidence mention message-level human-in-the-loop approval for live customer conversations.

   • [claimed-docs] “Agent Checks and Simulations catching problems proactively, merge approval workflows putting a person in the loop, and split traffic release…”
   • [claimed-docs] “Guide care reps through next steps—whether in chat or on calls—automatically capturing and updating customer context as work progresses.”
   • [claimed-docs] “Initiate workflows directly from a conversation—no tab-switching, no referencing instructions, no lost context.”

   proof ↗⚑ flag

Topic controls

1. support ops leadI mark topics as human-only — legal threats, cancellations, security — and the agent never freelances on them

   weight 2 · round to Fin
   Fin~partialclaimed6/10

   Fin has documented first-party features for exactly this: 'Giving Fin Guidance' and 'Manage Fin AI Agent's escalation guidance and rules' let ops teams define escalation/human-handoff rules, and the Agent API includes an 'Escalate to a human' capability. However this escalate-to-human orchestration endpoint is explicitly noted as Preview-only, and there is no independent or hands-on evidence confirming the agent reliably respects human-only topic boundaries without ever freelancing. Missing for 10: independent verification that escalation rules are strictly enforced (no freelancing), and GA (non-preview) status of escalate-to-human tooling.

   • [claimed-docs] “Giving Fin Guidance”
   • [claimed-docs] “Manage Fin AI Agent's escalation guidance and rules”
   • [claimed-docs] “The new orchestration endpoints — Discover capabilities, Ask Fin, Run a procedure, and Escalate to a human — are currently available in the …”

   proof ↗⚑ flag

   Sierra—none0/10

   Sierra's guardrail evidence covers release governance (Agent Checks, Simulations, merge approval, split traffic) and general knowledge/workflow editing, but nothing describes topic-level human-only flags for categories like legal threats, cancellations, or security that the agent is barred from handling. Live-assist shows human+AI collaboration but not a mechanism to designate certain topics as strictly human-only with enforced escalation.

   • [claimed-docs] “Agent Checks and Simulations catching problems proactively, merge approval workflows putting a person in the loop, and split traffic release…”
   • [claimed-docs] “Guide care reps through next steps—whether in chat or on calls—automatically capturing and updating customer context as work progresses.”

   proof ↗⚑ flag

Analytics

1. support leaderDashboards show resolution rate, CSAT, handoff rate, and cost per resolution — the numbers I report to my exec team

   weight 3 · round to Sierra
   Fin~partialclaimed4/10

   Fin's Insights/Monitors/AI Recommendations pages reference analyzing conversations and measuring CX, and CX Score/Trends are listed as features, implying some dashboard capability, but no evidence explicitly confirms a unified dashboard reporting resolution rate, CSAT, handoff rate, and cost per resolution together. missing for 10: explicit documentation or screenshots of a dashboard showing resolution rate, CSAT, handoff rate, and cost per resolution metrics; independent confirmation these specific KPIs are surfaced for exec reporting.

   • [claimed-docs] “Insights continuously analyzes every Fin and human conv”
   • [claimed-docs] “With Insights, Monitors New, and Recommendations, you have everything you need to understand, measure, and improve your customer experience”
   • [claimed-docs] “Operator, CX Score, AI Topics, Trends, AI Recommendations, Monitors, Custom AI Scorecards, Incident Detection”
   • [claimed-docs] “Fin resolves 76% of customer queries on average, and handles multi-step queries end to end. Your customers get fast, accurate answers. Your …”

   proof ↗⚑ flag

   Sierra~partialclaimed5/10

   Sierra's Insights and Explorer products provide dashboards/reports with drill-down and natural-language trend analysis (sierra-docs-10, sierra-docs-11, sierra-docs-12, sierra-docs-13), showing the platform surfaces analytics for support leaders, but no evidence specifies the exact metrics named in the story (resolution rate, CSAT, handoff rate, cost per resolution). Missing for 10: explicit documentation naming these specific KPIs, screenshots/examples of the actual dashboard metrics, and independent confirmation that leaders use it for exec-level reporting.

   • [claimed-docs] “Ask any question about your customer experience in natural language, and Explorer identifies the answer across thousands of real conversatio…”
   • [claimed-docs] “Explorer automatically delivers a weekly briefing on key trends, emerging issues, and recommendations — without you having to ask.”
   • [claimed-docs] “Click any data point on a report to launch Explorer and instantly investigate what's driving that trend.”
   • [claimed-docs] “Understand the reasoning behind every agent action or answer—view knowledge sources, systems accessed, and more.”

   proof ↗⚑ flag

Insights

1. support leaderThe platform clusters conversations by topic and surfaces emerging product issues before they spike ticket volume

   weight 1 · round drawn
   Fin~partialclaimed6/10

   Fin's pricing page lists analytics features like 'AI Topics, Trends... Monitors... Incident Detection' and 'Insights continuously analyzes every Fin and human conversation,' indicating topic clustering and emerging-issue detection capability, but there is no deeper documentation, screenshots, or independent corroboration of how this works in practice. Missing for 10: detailed product docs on topic clustering methodology, evidence of proactive alerting before ticket-volume spikes, and independent/hands-on validation of the insights/analytics feature.

   • [claimed-docs] “Operator, CX Score, AI Topics, Trends, AI Recommendations, Monitors, Custom AI Scorecards, Incident Detection”
   • [claimed-docs] “Insights continuously analyzes every Fin and human conv”
   • [claimed-docs] “With Insights, Monitors New, and Recommendations, you have everything you need to understand, measure, and improve your customer experience”

   proof ↗⚑ flag

   Sierra~partialclaimed6/10

   Sierra's Explorer product explicitly surfaces trends and emerging issues via natural-language querying of conversations and proactive weekly briefings, and Insights lets users drill into data points—directly matching the 'surface emerging issues before they spike' theme. However, evidence doesn't explicitly confirm topic clustering methodology, predictive spike detection, or independent/hands-on validation of this specific capability. Missing for 10: explicit description of topic-clustering mechanics, evidence of predictive alerting before ticket-volume spikes (vs. reactive weekly summaries), and independent/customer corroboration of this specific insights capability.

   • [claimed-docs] “Ask any question about your customer experience in natural language, and Explorer identifies the answer across thousands of real conversatio…”
   • [claimed-docs] “Explorer automatically delivers a weekly briefing on key trends, emerging issues, and recommendations — without you having to ask.”
   • [claimed-docs] “Click any data point on a report to launch Explorer and instantly investigate what's driving that trend.”

   proof ↗⚑ flag

Helpdesk

1. developerThe agent runs inside my existing helpdesk — Zendesk, Salesforce, Intercom — or standalone, syncing tickets and context both ways

   weight 3 · round to Fin
   Fin✓fullclaimed8/10

   Fin explicitly documents standalone deployment plus deep integrations with Zendesk, Salesforce, HubSpot, Freshdesk and 'any helpdesk', with two-way data sync (bring content/history over, surface data in inbox) and APIs/MCP/webhooks for programmatic ticket/context sync. Multiple dedicated integration pages (Zendesk, Salesforce, HubSpot, Freshdesk) and platform docs (Agent API, MCP, webhooks, data connectors) corroborate bidirectional context flow. missing for 10: independent/hands-on verification of the bidirectional sync working in production, and no direct evidence of Intercom-native standalone ticketing depth compared to third-party helpdesks.

   • [claimed-docs] “Fin AI Agent works seamlessly with any helpdesk, including Salesforce, HubSpot, and more.”
   • [claimed-docs] “How Fin integrates with Zendesk”
   • [claimed-docs] “How Fin integrates with Salesforce”
   • [claimed-docs] “bring your team over from any helpdesk without losing your content, your history, or a single day of support.”
   • [claimed-docs] “Surface data from Salesforce, HubSpot, and 450+ apps and integrations directly in the inbox”
   • [claimed-docs] “Fin can be accessed programmatically via an API. You call Fin from your o”
   • [claimed-docs] “Learn how to use the Model Context Protocol (MCP) to enable AI agents to securely access and interact with your Intercom data whenever helpf…”
   • [claimed-docs] “Webhooks allow you to subscribe to real-time notifications of events happening in Intercom; a Contact created, an incoming Conversation rece…”
   • [claimed-docs] “It works through MCP or API Data Connectors for your business tools, integrations with Intercom or your existing helpdesk”
   • [claimed-docs] “Intercom connects to 450+ first- and third-party apps and integrations, including CRMs like Salesforce and HubSpot, dev tools like Jira, Lin…”

   proof ↗⚑ flag

   Sierra—none0/10

   No evidence pack citation mentions Zendesk, Salesforce, Intercom, or bidirectional ticket/context syncing with existing helpdesk platforms; only generic 'systems integrations' and 'internal APIs' are referenced without naming any helpdesk system or describing two-way ticket sync.

   ⚑ flag

Freshness

1. support ops leadKnowledge stays current automatically — the agent re-syncs sources on a schedule or on change, not via manual re-uploads

   weight 2 · round to Fin
   Fin~partialclaimed5/10

   Docs mention 'Sync and manage websites' and content import via API (createContentImportSource), implying re-syncing of website sources, but there is no explicit documentation of scheduled or change-triggered automatic re-syncing across all source types (docs, help centers, files) without manual re-upload. missing for 10: explicit scheduling/cron documentation for content refresh, confirmation of automatic change-detection re-sync for non-website sources (PDFs, articles), and independent/hands-on verification that sync happens without manual re-upload.

   • [claimed-docs] “Sync and manage websites”
   • [github] “await client.aiContent.createContentImportSource({ url: "https://www.example.com" });”

   proof ↗⚑ flag

   Sierra—none0/10

   Evidence shows Sierra lets teams manually 'view, manage, and edit knowledge data' (sierra-docs-7) but nothing describes automatic re-syncing of knowledge sources on a schedule or on-change detection; docs are login-gated so no further detail is visible.

   • [claimed-docs] “View, manage, and edit knowledge data such as Help Center content, FAQs, and policies that ground your agent.”
   • [probe] “PROBE runtime finding (recorded 2026-09-10): docs.sierra.ai has no agent-legible surface — /llms.txt resolves (HTTP 200) to the login SPA's …”

   proof ↗⚑ flag

Gaps

1. support ops leadThe platform surfaces knowledge gaps and conflicting content that cause the agent to miss or fumble questions

   weight 1 · round to Sierra
   Fin~partialclaimed4/10

   Fin's analytics stack (Insights, Monitors, AI Topics/Trends, AI Recommendations, Incident Detection) implies some ability to flag where Fin underperforms or topics trend poorly, and 'Giving Fin Guidance'/'Adding content to Fin' suggest content curation workflows, but no evidence explicitly describes detecting knowledge gaps or conflicting source content as a distinct surfaced capability. Missing for 10: explicit documentation of a 'knowledge gap' or 'content conflict' detection feature, and any hands-on/independent confirmation that such gaps are surfaced to support-ops leads.

   • [claimed-docs] “Operator, CX Score, AI Topics, Trends, AI Recommendations, Monitors, Custom AI Scorecards, Incident Detection”
   • [claimed-docs] “Insights continuously analyzes every Fin and human conv”
   • [claimed-docs] “With Insights, Monitors New, and Recommendations, you have everything you need to understand, measure, and improve your customer experience”
   • [claimed-docs] “Giving Fin Guidance”
   • [claimed-docs] “Adding content to Fin”

   proof ↗⚑ flag

   Sierra~partialclaimed5/10

   Sierra's Explorer and Insights products surface emerging issues, trends, and the reasoning/knowledge sources behind agent answers, and Agent Studio lets teams view/manage knowledge content (FAQs, policies), which could help surface gaps — but no evidence explicitly describes detecting knowledge gaps or conflicting content as a distinct feature. Missing for 10: explicit conflicting-content/contradiction detection, explicit 'knowledge gap' flagging, and independent/hands-on confirmation that Explorer or Insights actually pinpoints such gaps rather than general conversation trends.

   • [claimed-docs] “View, manage, and edit knowledge data such as Help Center content, FAQs, and policies that ground your agent.”
   • [claimed-docs] “Ask any question about your customer experience in natural language, and Explorer identifies the answer across thousands of real conversatio…”
   • [claimed-docs] “Explorer automatically delivers a weekly briefing on key trends, emerging issues, and recommendations — without you having to ask.”
   • [claimed-docs] “Click any data point on a report to launch Explorer and instantly investigate what's driving that trend.”
   • [claimed-docs] “Understand the reasoning behind every agent action or answer—view knowledge sources, systems accessed, and more.”

   proof ↗⚑ flag

Grounding

1. ai-native userEvery answer is grounded in my own content and shows which article or source it drew from

   weight 3 · round to Sierra
   Fin~partialclaimed5/10

   Fin's docs confirm it is trained/grounded on the customer's own content (help center, websites, docs, knowledge sources) via content import and knowledge retrieval APIs (e.g., docs-46/60 'train Fin on your knowledge', docs-28 'Adding content to Fin', docs-27 'Sync and manage websites', gh-1 content import API, docs-39 knowledge retrieval API), but no evidence explicitly shows that end-user answers surface or cite the specific source article they were drawn from. missing for 10: explicit documentation or UI evidence of in-answer source citation/attribution, independent/hands-on confirmation of citation behavior.

   • [claimed-docs] “Train Fin on your knowledge, data, policies, and tone of voice.”
   • [claimed-docs] “Train Fin on your knowledge, data, policies, and tone of voice. Fin will use everything it knows to deliver consistent, accurate answers”
   • [claimed-docs] “Adding content to Fin”
   • [claimed-docs] “Sync and manage websites”
   • [github] “await client.aiContent.createContentImportSource({ url: "https://www.example.com" });”
   • [claimed-docs] “The Fin API platform includes APIs for answer generation and knowledge retrieval, available as a set or independently.”
   • [claimed-docs] “grounded in your knowledge base, and trained to apply your specific policies on every call.”

   proof ↗⚑ flag

   Sierra✓fullclaimed7/10

   Sierra explicitly supports grounding agent answers in customer-owned knowledge (Help Center content, FAQs, policies) via Agent Studio, and Insights lets teams 'understand the reasoning behind every agent action or answer—view knowledge sources, systems accessed' which directly maps to showing which source an answer drew from. Missing for 10: independent/hands-on verification that end-user-facing answers visibly cite specific articles, and detail on citation UX rather than just admin-side reasoning traces.

   • [claimed-docs] “View, manage, and edit knowledge data such as Help Center content, FAQs, and policies that ground your agent.”
   • [claimed-docs] “Understand the reasoning behind every agent action or answer—view knowledge sources, systems accessed, and more.”
   • [claimed-docs] “Ask any question about your customer experience in natural language, and Explorer identifies the answer across thousands of real conversatio…”

   proof ↗⚑ flag

Ingestion

1. support ops leadThe agent ingests my help center, docs, past tickets, and internal wikis as knowledge sources without manual re-authoring

   weight 3 · round to Fin
   Fin✓fullclaimed8/10

   Fin explicitly supports ingesting websites/help center content (createContentImportSource, 'Sync and manage websites', 'Adding content to Fin'), integrates with helpdesks (Zendesk, Salesforce, HubSpot, Freshdesk) preserving existing content/history, and pulls in Confluence, Notion, Zendesk content into a unified knowledge source alongside data connectors for third-party tools. This covers help center, docs, past ticket/helpdesk content, and wiki tools (Confluence/Notion) without manual re-authoring. Missing for 10: no explicit mention of ingesting 'internal wikis' broadly beyond Confluence/Notion, and no independent/hands-on verification of ingestion fidelity or effort required.

   • [github] “await client.aiContent.createContentImportSource({ url: "https://www.example.com" });”
   • [claimed-docs] “Sync and manage websites”
   • [claimed-docs] “Adding content to Fin”
   • [claimed-docs] “Bring content from your help center, Zendesk, Confluence, Notion, and more into one place, so Fin, Copilot, and your team always work from t…”
   • [claimed-docs] “bring your team over from any helpdesk without losing your content, your history, or a single day of support.”
   • [claimed-docs] “How Fin integrates with Zendesk”
   • [claimed-docs] “How Fin integrates with Salesforce”
   • [claimed-docs] “Your team stays on Freshdesk.”

   proof ↗⚑ flag

   Sierra~partialprobed6/10

   Sierra's docs explicitly claim agent-studio lets teams 'view, manage, and edit knowledge data such as Help Center content, FAQs, and policies' and Ghostwriter can ingest SOPs, transcripts, and audio interviews to build journeys, supporting knowledge ingestion beyond just help center docs. However, there's no explicit evidence of ingesting past tickets or internal wikis specifically, and docs/reference material is login-gated so independent verification of breadth of source-type ingestion is limited. Missing for 10: documented support for tickets/wiki ingestion specifically, and independent/hands-on confirmation of the ingestion workflow beyond marketing copy.

   • [claimed-docs] “View, manage, and edit knowledge data such as Help Center content, FAQs, and policies that ground your agent.”
   • [claimed-docs] “Upload SOPs, raw transcripts, or even audio interviews of subject matter experts to create customer journeys from scratch.”
   • [probe] “PROBE runtime finding (recorded 2026-09-10): docs.sierra.ai has no agent-legible surface — /llms.txt resolves (HTTP 200) to the login SPA's …”

   proof ↗⚑ flag

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

   weight 2 · round to Fin
   Fin~partialprobed4/10

   Fin exposes an Agent API, Node SDK, MCP, webhooks, and data-connector APIs for programmatic interaction, but there is no evidence of full UI/API parity — no OpenAPI spec was discoverable (404s on all candidate paths) and no documentation claims that every configuration (e.g., inbox setup, workflows, Procedures, escalation rules) manageable in the UI is also manageable via API. Missing for 10: comprehensive API reference/OpenAPI spec, explicit parity claims, evidence that admin/config UI actions (not just conversational/data actions) are API-accessible.

   • [claimed-docs] “Fin can be accessed programmatically via an API. You call Fin from your o”
   • [claimed-docs] “The new orchestration endpoints — Discover capabilities, Ask Fin, Run a procedure, and Escalate to a human — are currently available in the …”
   • [github] “The Intercom TypeScript library provides convenient access to the Intercom APIs from TypeScript.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://fin.ai/openapi.json, https://fin.ai/swagger.json, https://fin.ai/api/openapi.json, https://f…”
   • [claimed-docs] “The Fin API platform includes APIs for answer generation and knowledge retrieval, available as a set or independently.”

   proof ↗⚑ flag

   Sierra—none0/10

   Sierra markets an Agent SDK for code-based building alongside a separate no-code Agent Studio, but there is no evidence of API/UI feature parity — no public OpenAPI/swagger spec was found (404s), and the SDK reference docs themselves are login-gated to contracted customers, meaning even documented API scope can't be verified as matching UI capabilities.

   • [claimed-docs] “write customer journeys as code, track changes, and build sophisticated logic without giving up our development workflows”
   • [claimed-docs] “Empower teams to build and manage agents—no code required”
   • [probe] “PROBE openapi: all candidate paths 404 (https://sierra.ai/openapi.json, https://sierra.ai/swagger.json, https://sierra.ai/api/openapi.json, …”
   • [probe] “PROBE runtime finding (recorded 2026-09-10): docs.sierra.ai has no agent-legible surface — /llms.txt resolves (HTTP 200) to the login SPA's …”

   proof ↗⚑ flag

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

   weight 3 · round to Fin
   Fin~partialclaimed2/10

   The only export-related evidence is a narrow feature to 'Export a saved View exactly as you see it,' not a comprehensive data export mechanism; migration language found in the evidence is framed only around bringing data INTO Intercom from other helpdesks, not exporting all data out in open formats. Missing for 10: bulk/full account data export, documented open file formats (CSV/JSON), a migration-out or offboarding process, and any independent confirmation of successful full data extraction.

   • [claimed-docs] “Export a saved View exactly as you see it”
   • [claimed-docs] “bring your team over from any helpdesk without losing your content, your history, or a single day of support.”
   • [claimed-docs] “bring your team over from any helpdesk without losing your content, your history, or a single day of support”

   proof ↗⚑ flag

   Sierra—none0/10

   No evidence of any data export or portability feature for customer journeys, knowledge data, or conversation logs in open formats; probes even show docs/API surfaces are gated or unavailable (404s, login-gated /llms.txt). Missing for 10: any export functionality, data portability documentation, or open-format data dump capability.

   • [probe] “PROBE llms.txt: HTTP 404 at https://sierra.ai/llms.txt”
   • [probe] “PROBE openapi: all candidate paths 404 (https://sierra.ai/openapi.json, https://sierra.ai/swagger.json, https://sierra.ai/api/openapi.json, …”
   • [probe] “PROBE runtime finding (recorded 2026-09-10): docs.sierra.ai has no agent-legible surface — /llms.txt resolves (HTTP 200) to the login SPA's …”

   proof ↗⚑ flag

Pricing

1. support leaderPricing is outcome-based and published — I pay per resolution with caps and controls, not an opaque enterprise quote

   weight 2 · round to Fin
   Fin~partialcommunity4/10

   A community source states Fin charges '$1 per successful customer session', which is consistent with outcome-based, per-resolution pricing, and a dedicated fin.ai/pricing page exists (cited repeatedly for feature lists). However, none of the evidence shows the actual published rate table, caps, or usage controls on that pricing page — the citations only reference features (integrations, channels, AI Agent capabilities), not price mechanics or caps. Missing for 10: first-party documentation of the per-resolution rate/caps/controls on fin.ai/pricing, and independent corroboration beyond a single HN comment.

   • [community] “Fin charges $1 per successful customer session, incentivizing it to be helpful; at the time of an earlier interview Fin was reportedly still…”
   • [claimed-docs] “Fin AI Agent works seamlessly with any helpdesk, including Salesforce, HubSpot, and more.”

   proof ↗⚑ flag

   Sierra—none0/10

   No evidence pack item mentions pricing, resolution-based billing, caps, or published rates; docs and probes cover product features and access gating only. Sierra is widely known anecdotally for outcome-based pricing but nothing in this evidence pack substantiates published, capped, self-serve pricing terms.

   ⚑ flag

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

   weight 2 · round drawn
   Fin—none0/10

   The evidence pack contains extensive documentation on Fin's features, integrations, and compliance messaging (e.g., 'meet the world's leading compliance standards') but no mention of data residency, region selection, or storage location controls anywhere in the docs or community evidence.

   ⚑ flag

   Sierra—none0/10

   No evidence pack item addresses data residency, region selection, or storage location controls; the docs cover agent building, workflows, and channels but not data governance/residency options. missing for 10: any mention of data region selection, residency guarantees, or storage location controls.

   ⚑ flag

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

   weight 3 · round drawn
   Fin—none0/10

   No evidence in the pack addresses data being used for AI model training or an opt-out/data-use control; general trust/compliance mentions (e.g., trust-reliability page) do not specify training data usage or opt-out mechanisms.

   ⚑ flag

   Sierra—none0/10

   No evidence in the pack addresses data-training opt-out or AI model training policies; Sierra's docs focus on product features (agent building, analytics, channels) with no privacy/data-use policy statements provided.

   ⚑ flag

3. ai-native userControl data retention and deletion

   weight 2 · round drawn
   Fin—none0/10

   No evidence in the pack addresses data retention policies, deletion controls, or user-facing controls over how long data is kept or when it is deleted; the docs cover integrations, channels, MCP access, and training but never retention/deletion settings.

   ⚑ flag

   Sierra—none0/10

   No evidence pack item addresses data retention controls, deletion policies, or user-facing data lifecycle management; docs cover agent building, analytics, and channels but never mention retention/deletion settings, and docs.sierra.ai is login-gated so no public verification exists.

   ⚑ flag

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

   weight 2 · round drawn
   Fin—none0/10

   No evidence pack items mention telemetry opt-out, usage tracking controls, or privacy settings for Fin; only trust/compliance marketing language and unrelated docs are present.

   ⚑ flag

   Sierra—none0/10

   No evidence pack item discusses telemetry, usage tracking, opt-out controls, or privacy settings for AI-native/developer users; docs focus on product features and docs are gated behind login. Missing for 10: any documentation of telemetry collection practices, opt-out mechanism, or privacy controls.

   ⚑ flag

Personalization

1. support leaderAnswers use the customer's live data — plan, order status, account history — not just generic help articles

   weight 2 · round to Fin
   Fin✓fullprobed7/10

   Docs show Fin connects to live business data via data connector templates (Stripe, Shopify, Statuspage), MCP/API data connectors, webhooks, and CRM integrations (Salesforce, HubSpot, Zendesk) that surface data like plan/order status directly in the inbox, plus Procedures that let Fin execute multi-step logic against third-party systems to update/retrieve real account data rather than just canned articles. Missing for 10: independent/hands-on verification that live data grounding actually improves accuracy in practice, and no case study quantifying resolution based specifically on live account/order data vs. help-article content.

   • [claimed-docs] “Data connector templates are now generally available, with ready-to-install templates across Stripe, Shopify and Statuspage.”
   • [claimed-docs] “Track data connector health over time”
   • [claimed-docs] “Takes action to update external systems”
   • [claimed-docs] “See everything in one view in the inbox — pull in data from tools like Shopify and Stripe alongside the conversation, so agents never have t…”
   • [claimed-docs] “Surface data from Salesforce, HubSpot, and 450+ apps and integrations directly in the inbox”
   • [claimed-docs] “Procedures let you train Fin to handle queries with multiple steps, business logic and third-party systems from start to finish.”
   • [claimed-docs] “MCP is a protocol that enables AI tools and applications to securely access and interact with your Intercom data whenever helpful.”
   • [probe] “official MCP server documented at https://developers.intercom.com/docs/guides/mcp”

   proof ↗⚑ flag

   Sierra~partialcommunity7/10

   Sierra's docs and community evidence show the agent integrates with customers' internal APIs and systems (not just static knowledge), with Insights explicitly noting 'systems accessed' during agent actions, and Live Assist capturing live customer context. However, no evidence explicitly confirms real-time retrieval of specifics like plan, order status, or account history. missing for 10: explicit documented example of pulling live order/plan/account data, independent verification beyond one HN anecdote.

   • [claimed-docs] “Understand and rapidly adjust agent behavior by inspecting API calls, logic traces, and more.”
   • [claimed-docs] “Understand the reasoning behind every agent action or answer—view knowledge sources, systems accessed, and more.”
   • [claimed-docs] “Guide care reps through next steps—whether in chat or on calls—automatically capturing and updating customer context as work progresses.”
   • [community] “Having seen this set up for a customer, what is interesting is how it works with internal APIs for customers. It's all point and click in a …”

   proof ↗⚑ flag

Reasoning

1. support leaderThe agent asks clarifying questions and works through multi-step troubleshooting instead of dumping one canned answer

   weight 2 · round drawn
   Fin~partialcommunity6/10

   Fin's docs describe 'Procedures' that train it to handle multi-step queries with business logic across systems, and marketing claims it 'handles multi-step queries end to end,' supporting structured troubleshooting rather than single canned replies. However, no evidence explicitly documents Fin asking clarifying questions mid-conversation, and one HN commenter argues open-ended agentic reasoning is unnecessary versus deterministic workflows, adding some uncertainty. missing for 10: explicit documentation/example of clarifying-question behavior, independent hands-on proof of multi-step troubleshooting quality.

   • [claimed-docs] “Procedures let you train Fin to handle queries with multiple steps, business logic and third-party systems from start to finish.”
   • [claimed-docs] “Fin resolves 76% of customer queries on average, and handles multi-step queries end to end. Your customers get fast, accurate answers. Your …”
   • [claimed-docs] “Giving Fin Guidance”
   • [community] “A commenter with a decade of customer-service AI/NLP experience argued 99% of support tasks (order status, returns, troubleshooting) are ful…”

   proof ↗⚑ flag

   Sierra~partialcommunity6/10

   Sierra's docs describe building step-by-step, multi-step 'customer journeys' and workflows (sierra-docs-6, sierra-docs-9) rather than single canned replies, and community commentary confirms agents are configured to work through processes with internal APIs (sierra-comm-2). However, no evidence explicitly demonstrates the agent proactively asking clarifying questions or a hands-on troubleshooting transcript showing multi-turn dialogue in practice. Missing for 10: concrete transcript/demo of clarifying-question behavior, independent evaluation of troubleshooting depth, and explicit mention of clarification-seeking as a designed capability.

   • [claimed-docs] “Define step-by-step workflows for your agent from scratch, or use AI to instantly generate journeys from your existing operating procedures.”
   • [claimed-docs] “Upload SOPs, raw transcripts, or even audio interviews of subject matter experts to create customer journeys from scratch.”
   • [community] “Having seen this set up for a customer, what is interesting is how it works with internal APIs for customers. It's all point and click in a …”

   proof ↗⚑ flag

Resolution

1. support leaderThe agent fully resolves a meaningful share of conversations end-to-end — measured as resolutions, not mere deflections or bounces

   weight 3 · round to Fin
   Fin✓fullcommunity7/10

   Fin explicitly claims a resolution metric ("Fin resolves 76% of customer queries on average, and handles multi-step queries end to end") across multiple integration pages, plus dedicated testing/evaluation tooling ("evaluate every live conversation") and analytics (Insights, Monitors) framed around resolution outcomes rather than deflection. Community commentary raises philosophical skepticism about autonomous agents vs. deterministic workflows, but does not concretely contradict the stated resolution rate. Missing for 10: independent/third-party audit of the 76% figure, and clear definition distinguishing 'resolution' from deflection/bounce in the metric methodology.

   • [claimed-docs] “Fin resolves 76% of customer queries on average, and handles multi-step queries end to end. Your customers get fast, accurate answers. Your …”
   • [claimed-docs] “Perfect customer experiences no longer come with a migration project. Fin resolves 76% of customer queries on average... Your team stays on …”
   • [claimed-docs] “Perfect customer experiences no longer come with a migration project... Your team stays on HubSpot.”
   • [claimed-docs] “you test it across thousands of scenarios before anything goes live, roll out changes with control, and evaluate every live conversation... …”
   • [claimed-docs] “you test it across thousands of scenarios before anything goes live, roll out changes with control, and evaluate every live conversation”
   • [claimed-docs] “With Insights, Monitors New, and Recommendations, you have everything you need to understand, measure, and improve your customer experience”
   • [community] “A commenter with a decade of customer-service AI/NLP experience argued 99% of support tasks (order status, returns, troubleshooting) are ful…”

   proof ↗⚑ flag

   Sierra~partialprobed6/10

   Sierra's marketing emphasizes agent-driven end-to-end workflow execution (journeys, integrations, payments, guardrails, testing) implying resolution rather than mere deflection, and community evidence corroborates real production use handling customer processes end-to-end for at least one customer. However, no quantitative resolution-rate metrics, benchmarks, or third-party validation of 'meaningful share resolved end-to-end' are provided — tau-bench is a research benchmark, not a customer outcome metric. Missing for 10: published resolution-rate statistics, case studies with concrete resolution percentages, independent audits distinguishing resolution from deflection.

   • [claimed-docs] “Define step-by-step workflows for your agent from scratch, or use AI to instantly generate journeys from your existing operating procedures.”
   • [claimed-docs] “Upload SOPs, raw transcripts, or even audio interviews of subject matter experts to create customer journeys from scratch.”
   • [claimed-docs] “Collect card and ACH payments entirely over the phone with no IVR handoff.”
   • [claimed-docs] “Replace rigid IVR menus with an empathetic voice agent that understands customer context”
   • [community] “Having seen this set up for a customer, what is interesting is how it works with internal APIs for customers. It's all point and click in a …”
   • [community] “The magic isn't in a new LLM technology, it is in reliably productionizing a solution for real-world problems... fill in the gap of missing …”
   • [probe] “PROBE runtime (recorded 2026-09-10): Sierra's agent benchmark is genuinely open — the official sierra-research/tau-bench repository README f…”

   proof ↗⚑ flag

Voice

1. support leaderI control the agent's tone and brand voice, and it stays consistent across topics and languages

   weight 1 · round to Sierra
   Fin~partialclaimed6/10

   Docs support tone/brand control ("Customizable tone and answer length", "Train Fin on your knowledge, data, policies, and tone of voice") and multi-language support is documented ("Use Fin AI Agent in multiple languages"), plus multi-channel consistency claims (email, Slack, live chat, voice) suggest tone carries across surfaces. However, there is no independent evidence or hands-on validation that tone/brand voice actually stays consistent across topics and languages in practice — only vendor marketing claims. Missing for 10: independent/third-party validation of tone consistency, concrete examples of cross-language tone fidelity, and evidence of consistency across many topics rather than just a training feature description.

   • [claimed-docs] “Customizable tone and answer length”
   • [claimed-docs] “Train Fin on your knowledge, data, policies, and tone of voice.”
   • [claimed-docs] “Train Fin on your knowledge, data, policies, and tone of voice. Fin will use everything it knows to deliver consistent, accurate answers”
   • [claimed-docs] “Use Fin AI Agent in multiple languages”
   • [claimed-docs] “Fin for messenger brings the highest-performing AI Agent for customer service to live chat, delivering exceptional, on-brand support”
   • [claimed-docs] “Fin delivers unrivaled performance across every channel. Wherever customers reach out, Fin provides fast, accurate, personalized support”

   proof ↗⚑ flag

   Sierra~partialclaimed7/10

   Sierra provides explicit tone/brand-voice controls (Ghostwriter prompts for tone and style, Voice Personas tuned across 59 languages) plus testing/simulation tools to verify agent behavior across scenarios and avoid regressions, which supports consistency claims. However, all evidence is vendor-authored with no independent or hands-on verification that voice/tone actually stays consistent across topics and languages in production. Missing for 10: independent case studies or benchmarks confirming cross-topic/cross-language tone consistency, and detail on how brand-voice guardrails are enforced at scale.

   • [claimed-docs] “Build or modify agents by describing how you want them to behave. Update workflows, systems integrations, guardrails, tone, and style with s…”
   • [claimed-docs] “Use Voice Personas to design how your agent sounds, speaks, and connects, powered by a constellation of models tuned across 59 languages.”
   • [claimed-docs] “Verify your agent performs as expected across a wide range of scenarios and avoid regressions.”
   • [claimed-docs] “Agent Checks and Simulations catching problems proactively, merge approval workflows putting a person in the loop, and split traffic release…”

   proof ↗⚑ flag

Qa

1. support ops leadAI conversations get ongoing QA — scored samples, flagged failures, and a review loop that feeds fixes back into the agent

   weight 1 · round to Fin
   Fin✓fullclaimed7/10

   Fin ships explicit QA tooling: fin.ai/testing describes testing across thousands of scenarios before launch and evaluating every live conversation, fin.ai/analyze describes Insights, Monitors, and Recommendations continuously analyzing every conversation, and pricing docs list Custom AI Scorecards and Incident Detection alongside guidance and procedure-editing docs that let fixes feed back into the agent behavior. missing for 10: independent or hands-on verification of the scoring and review loop, and detail on how flagged failures are routed to specific fixes.

   • [claimed-docs] “you test it across thousands of scenarios before anything goes live, roll out changes with control, and evaluate every live conversation”
   • [claimed-docs] “you test it across thousands of scenarios before anything goes live, roll out changes with control, and evaluate every live conversation... …”
   • [claimed-docs] “Insights continuously analyzes every Fin and human conv”
   • [claimed-docs] “With Insights, Monitors New, and Recommendations, you have everything you need to understand, measure, and improve your customer experience”
   • [claimed-docs] “Operator, CX Score, AI Topics, Trends, AI Recommendations, Monitors, Custom AI Scorecards, Incident Detection”
   • [claimed-docs] “Giving Fin Guidance”
   • [claimed-docs] “Building Fin Procedures”

   proof ↗⚑ flag

   Sierra~partialclaimed7/10

   Sierra documents Agent Checks and Simulations that proactively catch problems, merge-approval workflows for human review, and split-traffic releases (sierra-docs-18), plus regression testing (sierra-docs-4) and Explorer/Insights tools that surface conversation trends, flag emerging issues, and explain agent reasoning (sierra-docs-10, -11, -13). Ghostwriter lets teams feed fixes back by updating workflows/guardrails via prompts (sierra-docs-8). Missing for 10: explicit description of scored/sampled QA reviews with quantitative scoring rubrics, and independent evidence of the review loop actually closing the gap between flagged failures and shipped fixes.

   • [claimed-docs] “Verify your agent performs as expected across a wide range of scenarios and avoid regressions.”
   • [claimed-docs] “Agent Checks and Simulations catching problems proactively, merge approval workflows putting a person in the loop, and split traffic release…”
   • [claimed-docs] “Ask any question about your customer experience in natural language, and Explorer identifies the answer across thousands of real conversatio…”
   • [claimed-docs] “Explorer automatically delivers a weekly briefing on key trends, emerging issues, and recommendations — without you having to ask.”
   • [claimed-docs] “Understand the reasoning behind every agent action or answer—view knowledge sources, systems accessed, and more.”
   • [claimed-docs] “Build or modify agents by describing how you want them to behave. Update workflows, systems integrations, guardrails, tone, and style with s…”

   proof ↗⚑ flag

Simulation

1. support ops leadI test the agent against historical tickets or simulated conversations before it faces real customers

   weight 2 · round to Sierra
   Fin✓fullclaimed7/10

   Fin has a dedicated testing capability documented at fin.ai/testing ("test it across thousands of scenarios before anything goes live") and a specific help article "How to preview and test Fin," directly matching the support-ops need to validate the agent before it faces customers. Missing for 10: independent/hands-on corroboration of the testing workflow and explicit mention of importing historical ticket transcripts as test scenarios rather than only generic 'scenarios'.

   • [claimed-docs] “How to preview and test Fin”
   • [claimed-docs] “you test it across thousands of scenarios before anything goes live, roll out changes with control, and evaluate every live conversation”
   • [claimed-docs] “you test it across thousands of scenarios before anything goes live, roll out changes with control, and evaluate every live conversation... …”
   • [claimed-docs] “Train Fin on your Procedures, knowledge, and policies, test performance before launch, deploy across every channel, then analyze and improve…”

   proof ↗⚑ flag

   Sierra✓fullprobed8/10

   Sierra explicitly documents 'Agent Checks and Simulations' and 'verify your agent performs as expected across a wide range of scenarios and avoid regressions' (sierra-docs-4, sierra-docs-18), directly matching pre-release testing against scenarios/regressions. This is further corroborated by Sierra's public tau-bench research benchmark for evaluating conversational agents on simulated user interactions (sierra-probe-rt-1), showing real investment in simulation-based testing methodology. Missing for 10: no explicit mention of testing against historical/real ticket transcripts specifically (only 'scenarios' and simulations), and no independent hands-on customer account of the simulation workflow in practice.

   • [claimed-docs] “Verify your agent performs as expected across a wide range of scenarios and avoid regressions.”
   • [claimed-docs] “Agent Checks and Simulations catching problems proactively, merge approval workflows putting a person in the loop, and split traffic release…”
   • [probe] “PROBE runtime (recorded 2026-09-10): Sierra's agent benchmark is genuinely open — the official sierra-research/tau-bench repository README f…”

   proof ↗⚑ flag