API Agent-Readiness Audit
A comprehensive checklist to ensure your API works well with AI coding agents. Find the issues blocking adoption before your users do.
Overview
AI coding agents like Claude Code, Cursor, and Codex are becoming the primary way developers discover and integrate APIs. When an agent recommends your API but the generated code fails, developers rarely try again. They switch to a competitor.
This playbook walks you through a systematic audit of your API from the perspective of AI agents. You will identify where agents struggle, why they fail, and what to fix first for maximum impact on developer adoption.
By the end of this audit, you will have a prioritized list of improvements that directly impact how well agents can use your API, backed by real data from sandbox testing rather than guesswork.
Prerequisites
Before you begin, make sure you have the following ready:
Lightsage account with API testing enabled
You'll need access to the Agent Usability module to run evals and view performance data.
API documentation URL
Your public API docs endpoint (e.g., docs.yourcompany.com or yourcompany.com/docs).
Test API credentials
A sandbox or test API key that agents can use during evaluation runs.
List of core endpoints
Identify your 10-20 most important endpoints that developers commonly integrate first.
Run baseline eval across all coding agents
Start by establishing your baseline. In Lightsage, navigate to Agent Usability > Performance Testing and create a new eval suite. Add your API documentation URL and configure your test API credentials. The system will automatically discover your endpoints and generate test scenarios.
Select all 12 coding agents for the baseline run. While it is tempting to start with just one or two, each agent has different behaviors, context window sizes, and tool-calling patterns. An endpoint that works perfectly in Cursor might fail consistently in Claude Code due to how it constructs multi-step requests.
Once configured, run the full eval suite. This typically takes 15-30 minutes depending on your API complexity. The system spins up real sandbox environments and tests whether each agent can successfully discover, authenticate, call, and parse responses from your API.
Pro tip: Custom eval scenarios
Beyond the auto-generated tests, add 3-5 custom scenarios based on your most common integration patterns. For example, if most users start with authentication then immediately list resources, create a scenario that tests that exact flow.
Identify high-failure endpoints
After your baseline eval completes, navigate to the Results Dashboard and sort endpoints by failure rate. Lightsage categorizes failures into actionable buckets: discovery failures (agent could not find the endpoint), authentication failures, parameter errors, and response parsing issues.
Export the endpoint failure report as CSV for detailed analysis. Look for patterns across agents. If an endpoint fails across all 13 agents, the issue is likely in your documentation or API design. If it fails only in specific agents, the issue might be related to how those agents construct requests or handle certain response formats.
Pay special attention to critical path endpoints: authentication, resource creation, and your most-used read operations. A 40% failure rate on authentication means 40% of developers using AI agents will hit a wall before they even start.
| Failure Type | Common Causes | Priority |
|---|---|---|
| Discovery | Missing from llms.txt, poor docs SEO | Critical |
| Authentication | Unclear header format, missing examples | Critical |
| Parameters | Ambiguous types, missing required fields | High |
| Response | Inconsistent schemas, undocumented fields | Medium |
Review llms.txt configuration
The llms.txt file is like robots.txt for AI agents because it tells them what your API can do and how to use it. In Lightsage, go to Agent Usability > llms.txt Analyzer to audit your current configuration.
The analyzer checks for completeness, accuracy, and common anti-patterns. Key things it flags: missing endpoint descriptions, outdated base URLs, authentication instructions that do not match your actual API, and rate limit information that agents need to respect.
A well-structured llms.txt dramatically improves agent discovery. Without it, agents rely on crawling your docs and inferring structure, which often leads to incorrect assumptions. The file should include your base URL, authentication method, a brief API overview, and links to your most important endpoints.
# YourAPI llms.txt
# Optimized for AI coding agents
> YourAPI provides payments infrastructure.
> Start with authentication, then create customers.
Base URL: https://api.yourcompany.com/v1
Authentication: Bearer token in Authorization header
## Quick Start
- POST /auth/token - Get access token
- POST /customers - Create a customer
- GET /customers/{id} - Retrieve customer
- POST /payments - Process payment
## Important
- All requests require Content-Type: application/json
- Rate limit: 100 requests per minute
- Sandbox available at sandbox.yourcompany.comCommon mistake: Over-documenting
Your llms.txt should be concise, not comprehensive. Agents have limited context windows. Include your 10-20 most important endpoints, not all 200. Link to full docs for edge cases.
Check documentation for agent blockers
Agent blockers are documentation patterns that confuse AI but seem fine to humans. In Lightsage, use the Docs Analyzer tool under Agent Usability to crawl your documentation and identify these issues automatically.
The most common blockers include: code examples that do not actually work (outdated SDK versions, typos, missing imports), authentication instructions split across multiple pages, parameter descriptions that say “see above” or reference other sections without links, and error codes without actionable resolution steps.
Review each flagged issue in context. The Docs Analyzer shows you exactly where agents got confused in your eval runs and lets you trace a failure back to the specific documentation page and paragraph that caused it. This makes fixing issues surgical rather than guesswork.
Common agent blockers to check:
Broken code examples
Examples that throw errors when copied verbatim
Fragmented auth docs
Auth steps scattered across multiple pages
Vague error messages
Errors like 'Invalid request' without specifics
Missing type definitions
Parameters without clear types or formats
Impact measurement
For each blocker found, Lightsage estimates the impact on your overall agent success rate. A broken auth example might show “Fixing this could improve success rate by ~15%” based on how many eval failures trace back to that specific issue.
Create fix priority list
Now synthesize everything into an actionable priority list. In Lightsage, go to Agent Usability > Recommendations to see an auto-generated priority list based on your eval results, llms.txt analysis, and docs audit.
Each recommendation includes an estimated impact score, effort level, and specific instructions. The impact score factors in how many agents are affected, how critical the failing endpoint is, and how often developers hit that code path. Focus on high-impact, low-effort fixes first. These are typically documentation clarifications and llms.txt updates.
Export your priority list and create tickets in your issue tracker. Lightsage integrates with Jira, Linear, and GitHub Issues to automatically create tickets with all the context your team needs. Schedule a re-eval for after your fixes ship to measure the actual improvement.
Sample priority matrix
Checklist summary
Use this checklist to track your progress through the audit:
- Run baseline evals across all 12 coding agents
- Export and analyze endpoint failure data
- Identify top 5 high-failure endpoints
- Review llms.txt for completeness and accuracy
- Audit authentication documentation clarity
- Check error message quality and consistency
- Verify SDK examples match current API version
- Test rate limit documentation accuracy
- Create prioritized fix list by impact score
- Schedule re-eval after fixes are deployed
Success criteria
A successful audit results in a 20%+ improvement in agent success rate within two weeks of implementing your top 3 priority fixes. Schedule your re-eval now to measure progress.
Continue your journey
After completing this audit, consider these related playbooks:
Ready to audit your API?
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