Control Token Spend Without Starving the Agent

Token budgets can cut cost or cut capability. ProofMap helps teams see which prompt and runtime changes actually preserve performance.

Why Choose ProofMap

Evaluate compressed prompts against the same objective criteria as the current production package.

Compare cost, token usage, and pass/fail outcomes across models and prompt variants.

Keep fallback paths for cases where budget cuts create unacceptable regressions.

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.

How do we reduce tokens safely?

Treat prompt compression as a candidate and run it through regression tests before approval.

Can token budgets vary by task?

Yes. Qualified mappings can route simple tasks to lean prompts while keeping richer prompts for harder cases.

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.

Cut token waste with evidence instead of arbitrary limits.