Route AI Work to the Right Runtime

Fallback routing should be based on evidence, not hope. ProofMap shows where challengers pass and where the baseline still earns its cost.

Why Choose ProofMap

Use objective criteria to decide whether a task can leave the baseline runtime.

Adopt cheaper or faster models for passing cases without forcing risky full migration.

Retain baseline fallback for failure-prone criteria and sensitive workflows.

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.

When should an AI agent fallback to another model?

Fallback is useful when a challenger fails critical criteria, when a provider is unavailable, or when a task needs a stronger runtime.

Can fallback routing reduce cost?

Yes. It lets teams reserve expensive models for the work that needs them while cheaper models handle qualified 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.

Use evaluation evidence to decide where every AI task should run.