Add deterministic chaos experiment runner#279
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resilience recovers from failures; this causes controlled ones and checks a steady-state hypothesis still holds (Chaos Toolkit lifecycle: verify before, inject faults, verify after, roll back LIFO). Probes/faults/ rollbacks are callables and the clock/RNG/sleep are injectable, so experiments run deterministically in tests. Wired through the facade, AC_run_chaos executor command (action-list spec), MCP tool and the Script Builder.
Up to standards ✅🟢 Issues
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| Metric | Results |
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| Complexity | 43 |
| Duplication | 0 |
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Summary
resiliencerecovers from failures (retry, circuit breaker); this is the inverse — it causes controlled failures and checks a steady-state hypothesis still holds. Modelled on the Chaos Toolkit lifecycle: verify steady state before, run the method (fault activities), verify after, then always run rollbacks (LIFO). Returns a journal.ChaosExperiment(title, probes, method, rollbacks),Probe(name, call, tolerance),Fault(name, apply).run_experiment(experiment, *, clock=)— journal{status, deviated, run, rollbacks, steady_states};failed-before-methodif the hypothesis fails up front.latency_fault/exception_fault— fault factories with injectable RNG (rate) + sleep.toleranceis a literal, a[low, high]range, or a predicate. Probes/faults/rollbacks are caller callables; clock/RNG/sleep injectable → deterministic tests (no real failures or sleeping). Probe/fault errors are caught and recorded. Pure stdlib (random+time).Five-layer wiring
je_auto_control/utils/chaos/__init__.py+__all__AC_run_chaos— action-list spec{title, probes:[{name, action}], method:[{name, action}], rollbacks:[[...]]}ac_run_chaosTests & docs
test/unit_test/headless/test_chaos_batch.py(10 tests: no-deviation, failed-before-method, deviation, tolerance range/predicate, exception fault, LIFO rollbacks, injectable clock, executor)Lint clean: ruff / pylint / bandit (B311 nosec, repo convention) / radon.