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Experience

Overview​

Experience is the online Router Learning evidence used by adaptation. It is kept in process on the request path and summarized from bounded outcomes and telemetry. Offline recipe learning can export this evidence for analysis.

Key Advantages​

  • Keeps expensive aggregation out of the request path.
  • Turns outcomes and telemetry into compact read-time evidence.
  • Gives adaptation a typed place for learned model-choice facts.
  • Can send detailed evidence to Router Replay when replay is enabled.

What Problem Does It Solve?​

Some routing evidence needs past requests: model fit, overuse, provider failures, latency, cache reuse, and effective cost. Reading all events during a request would be too slow. The router updates compact model experience in memory. When Router Replay is enabled, it also writes evidence for offline analysis; persistence requires a durable replay backend.

When to Use​

  • Adaptation needs aggregate evidence from outcomes and runtime telemetry.
  • An operator wants offline evals to explain cold-start model quality evidence.
  • Offline recipe learning needs bounded evidence for experiments.

Configuration​

The public API does not expose experience.enabled, experience.source, or a runtime seed-pack import field. If adaptation is enabled, model experience is included in routing_sampling.

Online experience is keyed by matched decision, decision tier, and model:

decision_id + decision_tier + model
  -> decision_tier + model
  -> model

Outcome ingestion updates model-targeted experience. Route, policy, stability, provider, and router outcomes feed replay diagnostics and offline recipe learning instead of directly mutating model quality.