Guardrails for AI Coding in API Development
Ship faster with AI coding agents - without shipping insecure APIs.
42Crunch provides the guardrails to ensure that engineering teams can confidently use AI coding assistants in API development and still meet the required quality and security standards before shipping.
The challenge with agentic AI for APIs
AI coding assistants and agents such as GitHub Copilot, Cursor, Claude Code and Windsurf can now scan source code, generate OpenAPI contracts, and automate large chunks of API development work. This obviously has lots of potential upside for improving productivity and accelerating the time to market for API-based services, yet unfortunately, the output when using these AI agents isn’t always complete or correct. Missing security details, inconsistent design choices, and undocumented behaviors can slip through — and the time you spend reviewing and fixing undermines any potential productivity gains.
AI agents accelerate tasks like:
- Generating OpenAPI contracts from code
- Filling in endpoints and schemas
- Updating specs as implementations evolve
But security and engineering teams routinely hit the same wall:
- Omissions: security schemes, auth requirements, error responses
- Inconsistencies: naming, formats, parameter handling
- Ambiguity: missing constraints that affect security and governance
- Review fatigue: humans must validate everything, every time
If you can’t trust the artefact, you can’t automate the pipeline.
42Crunch deterministic guardrails
With 42Crunch Audit we add an independent, consistent layer of validation to AI-generated API code. Audit provides a consistent standard for what “good” looks like in an API contract — independent of the agent variability.
Let the agent do the manual work inside the IDE
Use your preferred agentic tooling to generate or update your OpenAPI contract directly from code inside your IDE. In the video example here we use the popular Cursor agent.
Run a deterministic 42Crunch Audit
42Crunch Audit evaluates the contract for OAS quality and security completeness by automatically performing an analysis of AI agent generated output. It produces
- A clear score-card rating based on over 300 security checks
- A list of issues the agent missed
- Actionable guidance designed to remediate any gaps
Trust but verify - Feed audit results back to the agent
Instead of asking the agent to “make it better” generically, you give it specific, structured findings to fix. This context dramatically improves accuracy and reduces the continual back-and-forth that leads to productivity losses.
Re-run 42Crunch Audit to confirm fixes and ensure the contract meets your guardrail thresholds before it moves downstream (CI/CD, gateways, security reviews, production).
Outcomes you can expect
- Faster API delivery with less manual spec work
- Confidence that the eventual OpenAPI contracts generated are suitable for production use
- Less time spent reviewing AI output
- More consistent security posture across teams and services
- A repeatable, scalable pattern for agentic AI adoption in development
Build guardrails into your AI vibe coding workflow
Talk to us today about implementing guardrails for AI driven API development