Chaos engineering for AI systems

How it works

Production is hostile: tools time out, APIs return garbage, context gets truncated, retrieval comes back empty. Chaos engineering injects these faults on purpose and watches how the system copes — does the agent retry sensibly, fail safe, surface a clear error, or loop, stall, and hallucinate its way around the missing data? It targets the parts a happy-path test never reaches: recovery, self-correction, loop avoidance, latency under stress, and the overall user experience when things go wrong.

When to use it

Resilience testing of agentic and tool-using systems; validating retry, fallback, and timeout behaviour; before relying on a system in an environment you don't control — particularly for an agent that calls several external tools in sequence, where any one dependency failing partway through is a when, not an if.

Limitations

You can only inject the failure modes you anticipate, and running it against anything but an isolated harness risks real disruption to real users or real systems. Demonstrates resilience to the faults tested, not to the ones nobody thought to simulate.

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