PUNK

// implementation checklist

Measure repeated work before you reuse it.

Punk needs enough information to know when two tasks are truly the same, whether a proven result still applies, and when uncertain work should stay with the original model.

Product-fit boundary

Punk can receive supported model traffic through compatible endpoints and can collect richer context when integrations supply it. A gateway alone cannot infer hidden tool calls, identity, outcomes, or approval decisions your application does not report.

// baseline checklist

Validate the control point first.

Traffic fidelity

  • Supported request and response shapes remain compatible
  • Streaming, errors, retries, and usage are attributable
  • The configured live provider stays available for pass-through and fallback
  • Applications can identify the agent and environment that sent the call

Trace completeness

  • Messages, model, timing, cost, and final status are recorded
  • Tool calls and results are declared or instrumented where relevant
  • Identity, app, subject, policy, and freshness context are available
  • Declared side effects distinguish reads, writes, suppression, and approval

Operational use

  • Engineers can reconstruct the request path and failures
  • Costs and latency are associated with a route and workload
  • Evaluation or feedback can be attached to a specific run
  • Retention and redaction match the deployment’s data obligations

Future-route evidence

  • Candidate and live outputs can be compared without duplicate effects
  • Cache keys and artifacts are scoped by tenant and safety dimensions
  • A selected route states why it was eligible
  • Drift or mismatch returns the work to a safe path

// divide the jobs clearly

Gateway, observability, and safe reuse have related—but different—jobs.

Layer Primary question Useful evidence
AI gateway How does model traffic move, authenticate, and remain reliable? Compatibility, provider behavior, limits, retries, and request metadata
Agent observability What happened across models, tools, policies, and outcomes? Trace timeline, costs, latency, errors, evaluations, and feedback
Punk May this workload use a different path now? Scoped replay/shadow evidence, current policy, freshness, and fallback
Read the category comparisonOpen the glossary

// practical rollout

Instrument a useful slice before expanding coverage.

  1. Choose one representative agent

    Pick a workload with visible user impact, known tool boundaries, and a team that can judge whether the trace tells the truth.

  2. Connect supported model traffic

    Use the OpenAI-compatible integration or another supported path, then confirm basic request, response, cost, and failure fidelity.

  3. Add the context that changes safety

    Attach app and subject identity; instrument tool actions, approval state, and outcomes where those details determine whether reuse is safe.

  4. Review a real incident and a real success

    Before considering optimization, make sure a human can inspect why the route ran and what would be missing during debugging.

Find the work worth reusing.

Bring one real workload. Punk will show what repeats, what the opportunity is worth, and what should stay with the model.

Find what your agents can reuseSee a real example