How we build an autonomous SRE Agent for Kubernetes Deployments

LangChain developed an autonomous SRE agent to reduce the repetitive, cognitively demanding work of monitoring and troubleshooting Kubernetes clusters while preserving human control over infrastructure changes. The system performs low-cost proactive health checks by collecting cluster data with the Kubernetes Python client and using a single Claude Haiku call to generate structured Slack reports, while on-demand investigations use a Claude Sonnet orchestrator and parallel specialist subagents to analyze pods, scaling, performance, logs, security, and reliability. Its safety design separates autonomous read access from tightly controlled write access: all changes are proposed through a dedicated executor and require human approval in Slack, with the architecture reinforced by Kubernetes RBAC and narrowly scoped tools. The team chose Deep Agents and LangGraph for planning, subagent coordination, and built-in approval interrupts, while limiting expensive models and broad permissions to situations where they add value. LangSmith traces reveal costs, loops, false positives, and human edits, allowing the team to create regression datasets and evaluations from real incidents; LangSmith Engine further automates issue detection, proposed fixes, pull requests, and preventative tests. The open-source project is being used internally and introduced to some customers, with planned enhancements including durable approval state and stateful monitoring that retains recent incident context.

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