Cadre

Capstone 06 — DevOps Troubleshooting Agent for Kubernetes

AWS's DevOps Agent went GA, Resolve AI published its K8s playbooks, NeuBird demoed semantic monitoring, and Metoro tied AI SRE to per-service SLOs. The production shape is settled: an alert webhook fires, an agent reads telemetry, walks a graph of K8s objects, ranks root-cause hypotheses, and posts a Slack brief with approval buttons. Read-only by default. Every remediation gated by a human. This capstone is that agent, evaluated on 20 synthetic incidents and compared against AWS's Agent on three shared cases.

Capstone 30 hr Python (agent), TypeScript (Slack integration)

Prerequisites: Phase 11 (LLM engineering), Phase 13 (tools and MCP), Phase 14 (agents), Phase 15 (autonomous), Phase 17 (infrastructure), Phase 18 (safety)

#Problem

The 2025-2026 SRE narrative became: "AI agents triage incidents, humans approve remediations." AWS DevOps Agent, Resolve AI, NeuBird, Metoro, PagerDuty AIOps all ship this shape in production. The agent reads Prometheus metrics, Loki logs, Tempo traces, kube-state-metrics, and a knowledge graph of K8s objects. It produces a ranked root-cause hypothesis with telemetry citations in under five minutes. It never executes destructive commands without explicit human approval through Slack.

Most of the hard work is scoping and safety, not reasoning. The agent needs a read-only-by-default RBAC surface, a hardened MCP tool server, and audit logs of every command considered vs executed. It needs to know when it is outside its depth and escalate. And it has to run cheap enough that OOM-kill cascades do not generate a $5k agent bill.

#Problem

Keep going. The rest of this lesson is one step away.