AI-Native Chaos Engineering: How Agent Legions Redefine System Resilience Validation

In the private cloud IaaS scenario, infrastructure failures such as node power loss, disk failures, and network anomalies are 'inevitable,' and the key is whether the system can automatically recover. Traditional chaos engineering has three major pain points: high use-case design cost, manual execution and diagnosis, and missing analysis that hinders reuse. Existing tools almost all remain in the 'inject and recover' phase, with observation analysis, diagnosis and localization, and report output relying on manual effort, making resilience validation only a periodic special drill that cannot be conducted at high frequency or normalized.

The business consequence is that risks often surface as online incidents during the gap between two drills. Therefore, a new paradigm is needed: let AI drive the full-chain closed loop from trigger to recovery, rather than simply using AI to replace a single step.

The team defined three hard standards: full-chain AI-driven (no manual checkpoints), standardized protocol interaction (agents communicate via standard protocols), and 10x efficiency improvement (an order of magnitude, not 10%). Quantifiable acceptance goals include: zero human intervention in the full chain for a single case, single closed loop completed within half an hour, continuous discovery of unknown defects, and reusable closed-loop analysis results.

Business value is reflected in four layers: risk pre-positioning (digesting risks before customers encounter real failures), efficiency improvement (dozens of times efficiency gain per validation, with manpower reduced from full-time SRE involvement to only triggering and confirmation), experience accumulation (structured fault use-case library, diagnostic evidence chain, recovery playbooks), and scale replication (standardized agent integration protocol, new product integration as simple as plugging in a USB device).