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How Decagon’s scores are calculated

The full audit trail, recomputed from the verdict data at build time through the same code that produced the leaderboard: verdict × quality × story weight per cell, cells sum to dimension scores, dimensions blend into the PA Score. Every number on the product page is reproducible from this page alone; for why the formula looks like this, see the methodology.

verdict factors: full ×1.0 · partial ×0.6 · disputed ×0.3 · none ×0.0 · n/a excluded from both sides · cell points = weight × quality × factor · cell max = weight × 10

PA Score12/100

Agent-ready 9.2 × 0.30 = 2.76

API quality 4.3 × 0.20 = 0.86

Openness 0.0 × 0.20 = 0.00

Built-in AI 43.8 × 0.15 = 6.57

Automation 14.3 × 0.15 = 2.15

(2.76 + 0.86 + 0.00 + 6.57 + 2.15) ÷ (0.30 + 0.20 + 0.20 + 0.15 + 0.15) = 12.33 ÷ 1.00 = 12.3

Scores are stored to 1 decimal; the product page’s pills round to whole numbers for display. Each dimension below shows the stories, verdicts, and cited evidence behind its number.

Agent-ready9.2/100×0.30 of the PA blend

Outside-in: can YOUR agent reach and drive this product — API, MCP, CLI, headless runs, agent docs.

Point an agent at llms.txt or agent-oriented docsweight 2

2 (weight) × 6 (quality) × 0.6 (partial) = 7.2 of 20 max

  • [probe] https://decagon.ai/llms.txt“PROBE llms.txt: HTTP 200 at https://decagon.ai/llms.txt # Decagon Decagon is an enterprise-grade AI platform revolutionizing customer support through the use of advanced conve”
  • [probe] https://decagon.ai/openapi.json“PROBE openapi: all candidate paths 404 (https://decagon.ai/openapi.json, https://decagon.ai/swagger.json, https://decagon.ai/api/openapi.json, https://decagon.ai/.well-known/openapi.json)”

Run the product headlessly / in CI for automationweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

  • [claimed-docs] https://decagon.ai/product/integrations“no custom code required”
  • [claimed-docs] https://decagon.ai/product/integrations“with no custom code required”
  • [probe] https://decagon.ai/openapi.json“PROBE openapi: all candidate paths 404 (https://decagon.ai/openapi.json, https://decagon.ai/swagger.json, https://decagon.ai/api/openapi.json, https://decagon.ai/.well-known/openapi.json)”

Plug MCP servers into this product so it can use their toolsweight 3

3 (weight) × 3 (quality) × 0.6 (partial) = 5.4 of 30 max

  • [claimed-docs] https://decagon.ai/blog/getting-the-most-out-of-mcp“Bridging that gap requires an infrastructure layer purpose-built to curate, scope, and evaluate how tools are actually used.”

Connect an agent via an official MCP serverweight 3

3 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 30 max

  • [claimed-docs] https://decagon.ai/blog/getting-the-most-out-of-mcp“Bridging that gap requires an infrastructure layer purpose-built to curate, scope, and evaluate how tools are actually used.”
  • [probe] https://decagon.ai/openapi.json“PROBE openapi: all candidate paths 404 (https://decagon.ai/openapi.json, https://decagon.ai/swagger.json, https://decagon.ai/api/openapi.json, https://decagon.ai/.well-known/openapi.json)”

Use an official CLIweight 2

n/a — not applicable to this product: excluded from numerator and denominator

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Drive the product through a documented public APIweight 3

3 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 30 max

  • [probe] https://decagon.ai/openapi.json“PROBE openapi: all candidate paths 404 (https://decagon.ai/openapi.json, https://decagon.ai/swagger.json, https://decagon.ai/api/openapi.json, https://decagon.ai/.well-known/openapi.json)”
  • [claimed-docs] https://decagon.ai/product/integrations“no custom code required”
  • [claimed-docs] https://decagon.ai/product/integrations“with no custom code required”

Issue scoped/least-privilege API credentials for an agentweight 2

2 (weight) × 4 (quality) × 0.6 (partial) = 4.8 of 20 max

  • [claimed-docs] https://decagon.ai/security“Short-lived JWT tokens give AI agents real-time access to customer systems, scoped for minimal privilege and discarded after each session.”
  • [claimed-docs] https://decagon.ai/security“Decagon integrates with identity providers like Okta and Microsoft Entra, enabling secure access across systems without additional passwords.”

Build against official SDKsweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

  • [claimed-docs] https://decagon.ai/blog/getting-the-most-out-of-mcp“Bridging that gap requires an infrastructure layer purpose-built to curate, scope, and evaluate how tools are actually used.”
  • [claimed-docs] https://decagon.ai/product/integrations“no custom code required”
  • [claimed-docs] https://decagon.ai/product/integrations“with no custom code required”
  • [probe] https://decagon.ai/openapi.json“PROBE openapi: all candidate paths 404 (https://decagon.ai/openapi.json, https://decagon.ai/swagger.json, https://decagon.ai/api/openapi.json, https://decagon.ai/.well-known/openapi.json)”

Subscribe to events via webhooksweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

  • [probe] https://decagon.ai/openapi.json“PROBE openapi: all candidate paths 404 (https://decagon.ai/openapi.json, https://decagon.ai/swagger.json, https://decagon.ai/api/openapi.json, https://decagon.ai/.well-known/openapi.json)”

Agent-ready = 17.4 ÷ 190 × 100 = 9.2

API quality4.3/100×0.20 of the PA blend

The programmable surface once an agent is there — machine-readable spec, interactive docs, sandbox, versioning discipline.

Explore an interactive API reference with runnable examplesweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

  • [probe] https://decagon.ai/openapi.json“PROBE openapi: all candidate paths 404 (https://decagon.ai/openapi.json, https://decagon.ai/swagger.json, https://decagon.ai/api/openapi.json, https://decagon.ai/.well-known/openapi.json)”

Download a machine-readable API spec (OpenAPI or equivalent)weight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

  • [probe] https://decagon.ai/openapi.json“PROBE openapi: all candidate paths 404 (https://decagon.ai/openapi.json, https://decagon.ai/swagger.json, https://decagon.ai/api/openapi.json, https://decagon.ai/.well-known/openapi.json)”
  • [probe] https://decagon.ai/llms.txt“PROBE llms.txt: HTTP 200 at https://decagon.ai/llms.txt # Decagon Decagon is an enterprise-grade AI platform revolutionizing customer support through the use of advanced conve”

Test against a sandbox environment without touching production dataweight 1

1 (weight) × 5 (quality) × 0.6 (partial) = 3.0 of 10 max

  • [claimed-docs] https://decagon.ai/product/testing-qa“Simulations, Decagon's integrated testing suite, help teams validate agent behavior across channels before deploying to production and with every subsequent update.”
  • [claimed-docs] https://decagon.ai/product/testing-qa“Use Duet to easily generate tests covering diverse pathways that verify whether agents respond accurately, follow policies, and reflect your brand.”
  • [claimed-docs] https://decagon.ai/product/experiments“Define your variable and traffic split, then measure results against a stable control group directly in production.”

Rely on versioned APIs with a documented deprecation policyweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

  • [probe] https://decagon.ai/openapi.json“PROBE openapi: all candidate paths 404 (https://decagon.ai/openapi.json, https://decagon.ai/swagger.json, https://decagon.ai/api/openapi.json, https://decagon.ai/.well-known/openapi.json)”

API quality = 3.0 ÷ 70 × 100 = 4.3

Openness0.0/100×0.20 of the PA blend

Can you leave, inspect, or self-host — data export, open source, portability.

Do everything through the API that I can do in the UIweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

  • [probe] https://decagon.ai/openapi.json“PROBE openapi: all candidate paths 404 (https://decagon.ai/openapi.json, https://decagon.ai/swagger.json, https://decagon.ai/api/openapi.json, https://decagon.ai/.well-known/openapi.json)”

Export all of my data in open formats and leaveweight 3

3 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 30 max

  • [claimed-docs] https://decagon.ai/proactive-agents“Maintain customer context across sessions, complementing your systems of record with data portability and control.”
  • [probe] https://decagon.ai/openapi.json“PROBE openapi: all candidate paths 404 (https://decagon.ai/openapi.json, https://decagon.ai/swagger.json, https://decagon.ai/api/openapi.json, https://decagon.ai/.well-known/openapi.json)”

Read the product's source under an open licenseweight 2

n/a — not applicable to this product: excluded from numerator and denominator

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Self-host the core productweight 3

n/a — not applicable to this product: excluded from numerator and denominator

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Openness = 0.0 ÷ 50 × 100 = 0.0

Built-in AI43.8/100×0.15 of the PA blend

Inside-out: how agentic the product itself is for its users — built-in assistants, autonomous features.

Get AI-generated insights and suggestions from my data inside the productweight 2

2 (weight) × 8 (quality) × 1.0 (full) = 16.0 of 20 max

  • [claimed-docs] https://decagon.ai/product/insights-and-reporting“Ask open-ended questions like “Why are customers requesting refunds?” to instantly analyze customer conversations.”
  • [claimed-docs] https://decagon.ai/product/suggestions“It automatically detects knowledge gaps and generates content drafts based on how your best human agents resolved those same issues, so your help center stays ahead of customer needs.”
  • [claimed-docs] https://decagon.ai/product/insights-and-reporting“Customizable visual heatmaps... Quickly identify spikes or dips in key metrics.”
  • [claimed-docs] https://decagon.ai/product/suggestions“It automatically detects knowledge gaps and generates content drafts based on how your best human agents resolved those same issues”
  • [claimed-docs] https://decagon.ai/product/insights-and-reporting“Ask AI powers deeper analysis through flexible, conversational queries.”
  • [claimed-docs] https://decagon.ai/product/duet“Analyze past customer interactions to automatically generate Agent Operating Procedures (AOPs) rooted in actual customer needs.”
  • [claimed-docs] https://decagon.ai/product/duet“As conversations reveal gaps and new opportunities, Duet automatically iterates on your agent, from the first launch through every improvement after.”

Set up automations that run autonomously in the backgroundweight 2

2 (weight) × 6 (quality) × 0.6 (partial) = 7.2 of 20 max

  • [claimed-docs] https://decagon.ai/proactive-agents“Carry conversation history across sessions to pick up where you left off and make proactive recommendations based on customer signals.”
  • [claimed-docs] https://decagon.ai/proactive-agents“Initiate intelligent, on-brand calls that reliably reach customers at the right moments throughout their journey.”
  • [claimed-docs] https://decagon.ai/proactive-agents“Maintain customer context across sessions, complementing your systems of record with data portability and control.”
  • [claimed-docs] https://decagon.ai/product/watchtower“Whether you're monitoring for compliance risks, negative sentiment, or upsell signals, Watchtower reviews every interaction against your custom criteria and surfaces what matters most.”

Delegate tasks to a built-in AI assistant inside the productweight 3

3 (weight) × 5 (quality) × 0.6 (partial) = 9.0 of 30 max

  • [claimed-docs] https://decagon.ai/product/overview“Non-technical teams can architect and iterate on agent logic with Agent Operating Procedures (AOPs) and Duet”
  • [claimed-docs] https://decagon.ai/product/duet“Analyze past customer interactions to automatically generate Agent Operating Procedures (AOPs) rooted in actual customer needs.”
  • [claimed-docs] https://decagon.ai/product/testing-qa“Use Duet to easily generate tests covering diverse pathways that verify whether agents respond accurately, follow policies, and reflect your brand.”
  • [claimed-docs] https://decagon.ai/product/suggestions“It automatically detects knowledge gaps and generates content drafts based on how your best human agents resolved those same issues, so your help center stays ahead of customer needs.”

Operate the product with natural-language commandsweight 2

2 (weight) × 6 (quality) × 0.6 (partial) = 7.2 of 20 max

  • [claimed-docs] https://decagon.ai/product/watchtower“Define what matters using natural language. From "mentions of frustration" to "violations of data privacy policy," Watchtower understands your flagging criteria and applies your intent contextually.”
  • [claimed-docs] https://decagon.ai/product/aop“you can define agent behavior in natural language, the same way you train human agents with SOPs”
  • [claimed-docs] https://decagon.ai/product/watchtower“Define what matters using natural language. From "mentions of frustration" to "violations of data privacy policy," Watchtower understands your flagging criteria”
  • [claimed-docs] https://decagon.ai/product/insights-and-reporting“Ask AI powers deeper analysis through flexible, conversational queries.”
  • [claimed-docs] https://decagon.ai/product/insights-and-reporting“Ask open-ended questions like “Why are customers requesting refunds?” to instantly analyze customer conversations.”

Built-in AI = 39.4 ÷ 90 × 100 = 43.8

Automation14.3/100×0.15 of the PA blend

Depth of automation primitives — rules, scheduling, bulk operations, webhooks.

Perform bulk operations across many items at onceweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Define rules that trigger actions automatically on eventsweight 3

3 (weight) × 5 (quality) × 0.6 (partial) = 9.0 of 30 max

  • [claimed-docs] https://decagon.ai/product/watchtower“Define what matters using natural language. From "mentions of frustration" to "violations of data privacy policy," Watchtower understands your flagging criteria and applies your intent contextually.”
  • [claimed-docs] https://decagon.ai/product/watchtower“Define what matters using natural language. From "mentions of frustration" to "violations of data privacy policy," Watchtower understands your flagging criteria”
  • [claimed-docs] https://decagon.ai/product/watchtower“Whether you're monitoring for compliance risks, negative sentiment, or upsell signals, Watchtower reviews every interaction against your custom criteria and surfaces what matters most.”
  • [claimed-docs] https://decagon.ai/proactive-agents“Carry conversation history across sessions to pick up where you left off and make proactive recommendations based on customer signals.”
  • [claimed-docs] https://decagon.ai/proactive-agents“Initiate intelligent, on-brand calls that reliably reach customers at the right moments throughout their journey.”
  • [claimed-docs] https://decagon.ai/product/aop“you can define agent behavior in natural language, the same way you train human agents with SOPs”

Schedule recurring jobs or workflowsweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Version, review, and roll back my automationsweight 1

1 (weight) × 4 (quality) × 0.6 (partial) = 2.4 of 10 max

  • [claimed-docs] https://decagon.ai/product/overview“technical teams retain full visibility and control over guardrails, integrations, and versioning”
  • [claimed-docs] https://decagon.ai/product/overview“Non-technical teams can architect and iterate on agent logic with Agent Operating Procedures (AOPs) and Duet, while technical teams retain full visibility and control over guard”
  • [claimed-docs] https://decagon.ai/product/testing-qa“Simulations, Decagon's integrated testing suite, help teams validate agent behavior across channels before deploying to production and with every subsequent update.”

Automation = 11.4 ÷ 80 × 100 = 14.3