SLO & Policy Planning

Charon turns diagnostic evidence into target-aware SLO outcomes and runtime-policy decisions.

SLO zones

Blocked-ratio assessment is evaluated against target-class-specific thresholds and grouped into zones used by policy planning.

ZoneMeaningTypical action
AcceptableWithin expected toleranceKeep current strategy
WarningDegrading behaviorIncrease retries and warmup
CriticalSustained blocking riskEscalate acquisition mode

Planning sequence

  1. Start from a normalized report (investigate_har or equivalent report source).
  2. Infer requirements (infer_requirements / infer_requirements_with_target_class).
  3. Build policy (build_runtime_policy or plan_from_report).
  4. Map policy to acquisition hints (map_runtime_policy, adapter strategy mapping).

This makes policy behavior explicit, testable, and consistent across run environments.

Adaptive policy tuning

For long-running systems, use adaptive helpers:

  • AdaptiveSloPolicy for selecting and updating SLO posture.
  • RegressionHistoryPolicy for history-driven threshold behavior.

These APIs allow controlled drift handling instead of one-off threshold edits.

Snapshot compatibility and drift

Use snapshot helpers to keep identity checks deterministic:

  • schema compatibility validation for normalized snapshots
  • drift comparison utilities that focus on meaningful signal changes
  • fixture-backed checks in integration tests

These are especially useful when rolling stealth/profile changes that could affect anti-bot classification confidence.