Catch AI Agent Regressions Before Users Do

AI systems change even when your code does not. ProofMap turns prompt, model, and runtime changes into testable release events.

Why Choose ProofMap

Run objective-bound evaluations after prompt edits, model upgrades, or provider changes.

Review concrete failures instead of relying on aggregate scores alone.

Keep unqualified prompt and runtime mappings out of production.

Decision area	Ad hoc workflow	ProofMap
Model or provider change	Teams compare demos, skim logs, and make a judgment call under pressure.	Run baseline-versus-challenger evaluations and see pass/fail evidence before a change ships.
Cost and performance tradeoff	Savings, latency, and quality are discussed separately, usually without a shared source of truth.	Compare quality evidence with cost, runtime, and fallback options in the same qualification workflow.
Production approval	Prompts and model choices move through informal review or one-off scripts.	Only qualified prompt packages and runtime mappings are promoted for production use.
Incident readiness	Fallbacks are invented after prices change, providers fail, or behavior drifts.	Backup models, prompt mappings, and fallback policies are qualified before they are needed.

What causes AI agent regressions?

Prompt edits, model version changes, provider behavior shifts, tool schema changes, and context changes can all affect outcomes.

How is this different from unit testing?

ProofMap evaluates probabilistic agent behavior against objectives and evidence, not just deterministic code paths.

Who is this for?

Teams building AI agents or LLM-backed workflows that need evidence before changing prompts, models, providers, or fallback policies.

What does ProofMap produce?

A qualification trail: objective-bound evaluations, failure evidence, recommendations, and approved prompt or runtime mappings for production use.

Make every AI runtime change prove itself before release.