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Operational Workflows

Where development workflows help you ship, operational workflows help you keep things running: automated incident triage, log and metric correlation, root cause analysis, and guided resolution. Operations is fertile ground for agents because the work is diagnostic — gather signals, form hypotheses, narrow down — which maps cleanly onto agent reasoning loops.

At 3 a.m., the bottleneck in incident response is rarely fixing the problem — it's finding it. Correlating a latency spike with a deploy, a config change, and a downstream dependency is exactly the multi-source synthesis agents are good at, and exactly what a tired on-call engineer is bad at.

Structure​

The agent narrows the search space and proposes; a human approves anything that changes production state. The incident retro flows back into the context layer so the next similar incident is faster.

The Workflows​

Incident triage — classify severity, identify the affected service and likely blast radius, and route to the right team. A Router over alert streams that turns noisy pages into structured, prioritized incidents.

Log and metric correlation — pull signals across logs, traces, metrics, and recent deploys, and surface what changed around the time symptoms started. This is multi-source retrieval and synthesis — the agent's core strength.

Root cause analysis — form and rank hypotheses, then gather evidence for or against each one (ReAct in a loop). Grounded in past incidents from the context layer: "this signature matches an incident from March — it was a connection-pool exhaustion."

Guided resolution — propose remediation steps drawn from runbooks, with the on-call engineer approving each action. Plan-and-Execute with a human gate on every production-changing step.

Key Characteristics​

  • Read-only first — start with diagnosis, not action. An agent that explains an incident is enormously valuable and carries near-zero risk. Earn write access later.
  • Human gate on state changes — anything that touches production is proposed, never executed autonomously, until the eval record overwhelmingly justifies it.
  • Grounded in history — past incidents are the best training data you have. Every retro fed back makes the next triage sharper. Without this loop you've built an Amnesiac Agent.
  • Latency matters differently here — during an incident, a 30-second correct correlation beats a 2-second guess. Optimize for trustworthiness over speed.
  • Auditability is non-negotiable — every hypothesis, every piece of evidence, every proposed action must be logged. Ops is where accountability gaps cause real damage.

When to Use​

  • On-call toil is dominated by finding problems, not fixing them.
  • Signals are scattered across logs, metrics, traces, and deploy history.
  • You have a corpus of past incidents and runbooks to ground against.

Pitfalls​

  • Autonomous remediation too early — an agent that restarts services or rolls back deploys on its own, before it's earned trust, is one bad inference away from an outage it caused. Keep the human in the loop.
  • Hallucinated root causes — a confident, wrong RCA sends the team down a rabbit hole. Require the agent to cite evidence (Citation) for every hypothesis.
  • Alert-driven infinite loops — an agent reacting to alerts it caused. Build in circuit breakers.