Architecting Governed, Self-Evolving AI Agents on Google Cloud

Project Chrysalis is a Google Cloud research reference architecture for “self-evolving AI” agents designed to respond to distributed production outages more autonomously than static runbooks or advisory copilots. Using a simulated Black Friday cache-stampede scenario in which Redis cache evictions overload Cloud SQL connection pools, the system diagnoses causes, generates and validates an in-memory token-bucket rate-limiting patch, evaluates the effects of each intervention, stores signed operational lessons, and—after human approval—rewires its multi-agent structure to add specialized traffic-shaping capabilities. Its approach combines BigQuery-based counterfactual policy simulation, three-layer governed evolution spanning temporary hotpatches, persistent principles, and agent-topology updates, and step-level Shapley attribution to distinguish beneficial actions from harmful ones such as database restarts. The demonstration reports that the architecture reduced simulated mitigation time to 1.8 seconds, eliminated HTTP 5xx errors after applying traffic shaping, lowered P99 latency to 18.2 milliseconds, and tested 400 candidate policies in roughly 0.5 milliseconds. Security and governance controls include Model Armor for telemetry sanitization, AST validation, Cloud KMS signatures and Binary Authorization for generated code, OPA-enforced service-level guardrails, dual-custody SRE approval for structural changes, and tamper-evident Cloud Logging audit records.

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