See complete work
Capture requests, model responses, tools, cost, latency, identity, policy decisions, and outcomes as one replayable history.
// category definition
AI agents need an adaptive runtime.
Models reason. Frameworks assemble. Gateways connect. An adaptive runtime learns from what agents actually do in production—and turns proven experience into better execution.
Adaptive runtime / definition
An infrastructure layer that observes agent execution, applies policy, identifies repeated work, tests alternative paths, and routes future requests through verified execution while preserving live fallback and an explanation of every decision.
// the missing layer
Agents should accumulate operational knowledge.
Most agent systems begin every run as if the last run never happened. Logs describe the past, but the execution path stays expensive, opaque, and unchanged. The adaptive runtime closes that loop.
Find what repeats
Detect stable request shapes, reusable tool plans, cacheable reads, and outputs that can become tested workflows.
Earn a better route
Test candidates against history and fresh traffic. Only scoped, policy-allowed paths become eligible to serve.
// category boundaries
Not another model, framework, or dashboard.
| Layer | Primary job | What remains missing |
|---|---|---|
| Model | Generate and reason | Does not govern the full application or learn a tenant's execution patterns. |
| Agent framework | Compose prompts, models, and tools | Defines the application; it does not independently prove production optimizations. |
| AI gateway | Provide access, routing, and reliability | Often routes calls, but does not turn repeated multi-step work into verified reusable execution. |
| Observability | Show traces, evaluations, and failures | Explains what happened; it does not necessarily change what should run next. |
| Adaptive runtime | Observe, govern, prove, and reuse execution | Connects evidence from prior runs to the route selected for the next one. |
Works with the stack you already have. Punk is OpenAI- and Anthropic-compatible. The category complements models, frameworks, gateways, and observability rather than asking teams to replace them all.
// operating principles
Adaptation needs limits.
A runtime should not become more autonomous merely because it has more data. Punk's adaptation is bounded by evidence, identity, policy, and fallback.
Tenant-local
Routes, traces, policies, caches, and reusable workflows stay scoped to the tenant and relevant app or subject boundaries.
Proof-first
Historical replay and fresh shadow comparison produce bounded evidence—not a universal correctness claim.
Policy-aware
Tool actions carry risk levels. Unknown tools default to a conservative user-visible-write posture.
Fails open
When an optimized path is not eligible, the request returns to the configured live provider.
// a durable architecture
The technical detail belongs here.
The runtime is a loop, not a single cache. Each layer contributes evidence or control that can be rebuilt from the append-only trace history.
Observe execution
The compatible gateway records complete agent traffic with app, agent, and subject identity.
Govern actions
MeshGuard-compatible policy evaluates model and tool behavior; consequential actions can be held for approval.
Structure the web
The Plasmate semantic web runtime turns pages and interactions into machine-usable state instead of brittle visual guessing.
Induce reusable execution
Punk can propose declarative, interpreted workflows from repeated behavior. It does not generate or evaluate executable code.
Replay, shadow, and route
Candidates are tested on historical and fresh traffic. Eligible requests can then use exact, semantic, tool, plan, artifact, hybrid, or model-substitution paths.
Explain and learn again
Every response includes a route explanation. Outcomes return to the ledger and improve the next decision.
Make every run teach the next one.
Start observe-only, establish your baseline, and discover whether one narrow workload can earn a better route.