Case study

Autonomous Incident Response Agent

An AI agent that triages production alerts, reconstructs root cause and opens a reviewed pull request — removing the overnight on-call rota without removing the human approval step.

Client type
B2B SaaS platform with a distributed engineering team (sample scenario)
Industry
SaaS
Duration
6 months

Sample contentSample engagement. Illustrative scenario used while the site is in build — not a named client project.

saas2026

Autonomous Incident Response Agent

  • Python
  • TypeScript
  • LLM integrations
  • RAG

The platform served users in one hemisphere while the engineering team lived in another. Overnight alerts waited for a person to wake up, and the first hour of every incident went into reconstructing context: which deploy, which service, which similar incident last quarter.

An agent that mirrors what an on-call engineer actually does — reads the alert and stack trace, pulls the related deploy diff and prior incidents, proposes a root cause, and opens a draft pull request with a test. Merge stays a human decision.

The overnight rota was retired, incident cost moved from unpredictable engineering hours to a fixed running cost, and senior engineers stopped starting their day inside someone else's outage.

  1. 01Two weeks of shadowing real incidents to record the actual diagnostic sequence
  2. 02Evaluation set built from a year of resolved incidents before any automation shipped
  3. 03Retrieval over service ownership, deploy history and past post-mortems
  4. 04Draft pull requests only — no path to production without human review
  5. 05Confidence thresholds that escalate ambiguous incidents straight to a person

The boundary that made it acceptable

The agent never merges. It prepares: diagnosis, patch, test, and the evidence behind them. Keeping the merge gate human is what got the design past both the engineering leads and the risk owner.

Evaluation before autonomy

Every diagnosis is scored against a labelled set of real, already-resolved incidents. A prompt or model change that degrades accuracy shows up in the harness before it shows up in an outage.

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Typical first step: a 30-minute call, then a short written assessment.