Keep your current agent
Send supported model requests through Punk. Your existing models remain in place while Punk learns from real work.
// the simple idea
Your agents should learn from work they have already finished.
Punk learns from completed agent work and turns successful reasoning, research steps, tool plans, and decisions into reusable know-how. New or uncertain work still goes to your original model.
Why this page has an old name
Some earlier material called Punk an “adaptive runtime.” We now use plainer language: Punk helps AI agents learn from completed work.
// what Punk does
Stop paying for the same reasoning again and again.
Many agent requests are new. Others are close versions of work the agent has already completed successfully. Punk learns the difference.
Find useful repetition
Punk groups recurring requests and processes, then identifies where a proven result could avoid unnecessary model work.
Serve proven work
Work that passes your quality and safety checks can run faster and at lower cost. Everything else stays with the model.
// one product promise
Make every successful run improve the next one.
Punk is not asking you to replace your models, frameworks, or monitoring tools. It adds a learning layer that turns successful past work into a safe option for future requests.
Spend less
Avoid a full model call when proven work can answer the request.
Respond faster
Return an eligible saved result or repeat a known process without starting over.
Stay dependable
Test improvements before use, keep uncertain work live, and record why each decision was made.
// when it fits
Start with work that repeats and can be checked.
A good first workload
- Runs often enough to reveal a pattern
- Has clear inputs and reviewable outputs
- Costs enough for reuse to matter
- Can safely return to the current model when uncertain
Work that may stay live
- Novel requests that need fresh reasoning
- Fast-changing information with no safe freshness rule
- Preference-heavy work without a clear quality check
- High-impact actions that require a person or policy decision
No universal savings claim. Punk measures your workload before recommending reuse. Some work repeats; some does not.
Find out what your agents have already learned.
Bring one real workload. We will identify what can become reusable, the possible savings, and what should remain with the model.