AgentGrade

Manual scoring framework

AgentGrade helps you assess whether an AI agent can realistically use your product.

AgentGrade does not fetch evidence or verify claims automatically. The methodology is a scoring framework for the user to apply with their own docs review, testing, and product knowledge.

Methodology principles

  • Practical over aspirational
  • Evidence over sales claims
  • Product usability over vague readiness language
  • Actionability over long reports

Scores should be based on the surfaces that matter in real agent workflows: discovering capabilities, authenticating, taking safe actions, handling failures, and reacting to changes. The goal is not to produce a perfect theoretical rating. The goal is to make a manual assessment legible and actionable.

Scoring categories

CategoryWhat it checks
API readinessWhether the API exposes the workflow with predictable inputs, outputs, and machine-usable behavior.
Auth frictionWhether access can be granted and maintained without brittle manual workarounds.
Action safetyWhether write paths include safeguards, reversibility, or risk-reduction patterns.
Docs clarityWhether an agent builder can implement from the docs and examples you reviewed without hidden assumptions.
Webhook / event supportWhether the product exposes useful state changes for reactive workflows.
Sandbox / demo availabilityWhether there is a safe place to test before touching production.
Rate-limit transparencyWhether limits and throttling behavior are clear enough for agent adaptation.
Error recoveryWhether structured errors and retry-friendly responses help agents recover.
MCP readinessWhether the product shows a practical path into emerging tool-based agent ecosystems.

Confidence matters

Every score should be read together with confidence.

  • High confidence: based on direct evidence such as current docs, hands-on testing, or clear product references.
  • Medium confidence: based on useful but incomplete evidence, such as partial docs or limited testing.
  • Low confidence: based on thin, indirect, or outdated public signal.

A low-confidence high score should not be treated as a strong result.

How to read the result

A strong score usually means your evidence suggests the product exposes the workflow through a usable API, access can be granted cleanly, agents can act with clear boundaries, docs reduce ambiguity, and event and test support make automation more reliable.

A weak score usually means part of the workflow is still human-only, authentication is hard to automate safely, there are few safeguards around writes, docs leave too many unknowns, or testing and operational controls are weak.

AgentGrade is informational. It is not a legal opinion, security certification, compliance certification, or guarantee of production safety. It also does not claim to have independently reviewed the submitted product unless a separate human review process exists outside this page.