Release AI Code Generation With Gates
Code generation agents can create real risk fast. ProofMap helps teams define and enforce evidence-backed release gates.
Get StartedWhy Choose ProofMap
Test correctness
Evaluate generated code, tool calls, structured outputs, and task completion against objective criteria.
Test safety
Check for insecure suggestions, policy violations, and failure to escalate risky changes.
Approve by runtime
Qualify which models and prompt packages are safe for each coding workflow.
Comparison
| Moment | Without ProofMap | With ProofMap |
|---|---|---|
| Evidence request | Teams assemble screenshots, anecdotes, and raw logs after the question arrives. | Qualification reports show prompt, model, tool, fallback, and approval evidence. |
| Production change | Prompt, model, schema, or permission changes are reviewed informally. | Changes run through objective-bound evaluations before promotion. |
| Business pressure | Audits, launches, renewals, and customer escalations force rushed AI decisions. | Teams use existing tests and approved mappings to respond with confidence. |
| Developer workload | Developers chase failures across transcripts, tools, providers, and one-off integrations. | Failures become repeatable tests with clear evidence and approved fixes. |
Frequently Asked Questions
Why gate code generation releases?
Generated code can introduce bugs, security issues, and developer frustration if behavior is not tested before rollout.
Can this work across coding models?
Yes. ProofMap can compare coding runtimes and qualify model-specific prompt packages.
What makes this useful for developers?
It turns AI behavior changes into repeatable tests, reduces manual investigation, and provides concrete evidence for prompt, model, MCP, and runtime decisions.
What does ProofMap produce?
ProofMap produces objective-bound evaluations, failure evidence, recommendations, and approved prompt or runtime mappings for production use.