Respond Fast When AI Runtime Behavior Changes

Sometimes a provider changes behavior before your team changes code. ProofMap helps diagnose the drift and move to a qualified fallback.

Why Choose ProofMap

Use existing evaluation suites to compare current behavior against the last trusted baseline.

Pinpoint which criteria, prompts, or tool behaviors changed.

Switch to a qualified prompt or runtime mapping while the root cause is investigated.

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 counts as an AI runtime incident?

Provider outages, unexpected behavior changes, latency spikes, price shocks, model deprecations, and degraded tool-use behavior can all qualify.

How quickly can we respond?

Teams with existing objectives and qualified fallbacks can move much faster because the evidence path already exists.

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.

Keep objective tests and fallback mappings ready for the day behavior changes unexpectedly.