What repeated?
See request families and recurring processes that account for meaningful model spend.
// evidence you can inspect
Know what Punk learned, when it applied that knowledge, and what it saved.
Punk makes its optimization visible. See which requests used a proven capability, which stayed with the model, how each decision performed, and whether estimated savings became real savings.
// the questions Punk answers
Trust starts with a result you can explain.
What was reused?
See when Punk returned proven work instead of paying for another full model call.
Why this choice?
See why Punk reused prior work, kept the model live, or stopped for a safety decision.
What changed?
Compare actual cost, response time, fallback, and quality signals for the workload.
// from estimate to result
Do not confuse possible savings with earned savings.
Punk first watches while your model continues answering. Once a reuse option has been tested and activated, Punk reports the savings it actually produced separately from earlier forecasts.
Establish the baseline
Measure the workload’s volume, model cost, response time, and available quality signals.
Learn from completed work
Identify successful reasoning, research paths, tool plans, and decisions that may improve future runs.
Test the proposed reuse
Compare it with accepted past results and fresh requests without changing the answer users receive.
Measure what actually served
Once approved, report only the cost and time saved by reuse that truly handled live requests.
// useful alongside your existing tools
Punk is not trying to replace every dashboard.
Keep the monitoring and evaluation tools your team already trusts. Punk adds the focused evidence needed to decide whether repeated work is safe to reuse—and to show the result afterward.
Your monitoring tools
Help teams explore failures, debug agent behavior, review quality, and operate the broader application.
Punk
Finds costly repetition, tests whether it can be reused, makes the live reuse decision, and measures the resulting savings.
Integration depth matters. Compatible model requests provide the starting point. Tool actions, identity, approvals, and business outcomes require the corresponding information from your application.
See what your agents can learn from their own work.
Start with one workload and get a clear view of the opportunity before changing how any request is answered.