October 2026 Summaries

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Cockroach Aegis is a preview feature within Cockroach Continuum that uses always-on intelligent agents to monitor CockroachDB clusters, learn normal workload behavior, detect operational anomalies, investigate likely root causes, and provide evidence-based remediation recommendations without making changes itself. Designed to help teams manage large deployments that may contain many isolated virtual clusters, it aims to reduce reliance on specialized DBA expertise by automatically conducting adaptive check-ins, tracking cluster history, and identifying issues such as stale statistics, unused or missing indexes, query contention, node restarts, and configuration drift. Aegis produces health reports and deduplicated recommendations with supporting metrics, documentation links, and often runnable SQL, while also supporting cluster-aware natural-language questions. Its hosted MCP server and CLI allow other tools and agents to access structured cluster context or delegate CockroachDB-specific tasks. The service is read-only, uses a dedicated auditable SQL identity with limited metadata access, excludes application table data and row values, isolates customer instances, and states that customer data is not used to train or fine-tune AI models.

CockroachDB and AWS hosted a Devpost hackathon focused on building AI agents with persistent memory, requiring participants to use at least two CockroachDB tools and one AWS service. About 3,700 developers registered and submitted more than 600 projects, with 78 finalists evaluated for memory design, technical implementation, real-world impact, and production readiness. First-place winner Anchor uses CockroachDB vector indexing and AWS Bedrock to help diagnose and remediate CockroachDB Cloud incidents while transactionally preserving reliable records of past fixes. Second-place Unsay is a medication-safety agent that uses bitemporal CockroachDB data and AWS Lambda to identify when changing FDA information invalidates prior answers and generate corrections. Third-place Interlock addresses conflicts among multiple AI coding agents by identifying and selectively repairing the effects of colliding code changes rather than discarding broader work.