AI agent cost optimization
Find costly repetition without treating every low-cost route as an optimization opportunity.
// resource library
Practical guides for production agent decisions.
Use these guides to define a safe first workload, assess the evidence behind an optimization, and ask better questions of the infrastructure that carries your agents.
How to use this library
Each guide covers a distinct operating decision. They explain the boundary between general practice and what Punk can support; they do not substitute for testing your models, tools, policies, or deployment.
// choose the question
Start with the decision in front of you.
Gateway + observability checklist
Decide what a production trace must contain before it can support routing or audit decisions.
Safe AI model routing
Design a routing policy around scope, quality evidence, fallback, and action risk—not price alone.
Replay + shadow evaluation
Separate historical qualification from fresh, side-effect-suppressed comparison.
Enterprise agent governance
Turn policy, identity, approval, and audit requirements into reviewable execution controls.
Infrastructure glossary
Definitions for the category language used across agents, gateways, replay, shadowing, and adaptive runtimes.
// product-fit boundary
Good guidance should leave room for “keep it live.”
Punk is designed for teams with supported, repeatable, reviewable model work. It starts by observing traffic and preserving the configured provider response. High-impact actions, incomplete traces, volatile data, and preference-heavy work may remain live or require human approval.
Bring one real workload.
Start free to inspect supported traffic in observe-only mode, or request a scoped workload review when you need a guided pilot.