AgentFleet

Incident → postmortem first draft

The tedious first draft of a postmortem writes itself from the incident timeline.

DevOps & Infrastructure4.2(5 reviews)32.8k

Overview

After an incident is resolved, an agent assembles a timeline from alerts, Slack messages, and deploy logs, and produces a first-draft postmortem for the on-call engineer to refine.

How this workflow works

Problem it solves
Postmortems get skipped or delayed because reconstructing the timeline by hand is tedious.
Expected outcome
A structured first-draft postmortem with a timeline exists within an hour of an incident closing.
Setup time
1 hour

Steps

  1. 01
    Incident resolved
    The incident is marked resolved in the incident tool.
  2. 02
    Assemble timeline
    An agent pulls alert timestamps, Slack thread messages, and deploy events into one timeline.
  3. 03
    Draft postmortem
    A first draft with timeline, impact, and open questions is generated for the on-call engineer.

Prerequisites

  • Incident management tool
  • Slack export access
  • Deploy log access

This workflow uses

Slack MCP Server

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Read and post to Slack channels from an agent.

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Reviews (5)

Marcus Ghannam
September 28, 2026

Would recommend with caveats

Good value for the price, and the team behind it ships improvements fast. Just don't expect it to read your mind about internal conventions on day one.

Pros
  • Frequent updates
  • Fair pricing
Cons
  • Some rough edges in edge cases
Dana Okafor
September 28, 2026

Exactly what we needed

Replaced a manual process that was eating an hour a day across the team. Straightforward to configure and it's held up under real usage.

Pros
  • Saves real time weekly
Sam Rivera
September 28, 2026

Useful but needs guardrails

Works well for the narrow case we use it for. We had to add explicit boundaries after it did something a bit too enthusiastic in week one.

Pros
  • Capable when scoped well
Cons
  • Needs explicit boundaries
  • Support response was slow
Leo Tan
September 16, 2026

Solid, with a learning curve

Took about a week to tune the config to our repo's conventions, but once it clicked it's been reliable. Wouldn't hand it the riskiest changes unsupervised yet.

Pros
  • Good defaults
  • Responsive to feedback
Cons
  • Setup docs assume more context than we had
Priya Krishnan
September 16, 2026

Genuinely changed how our team ships

We put this in front of real work for three weeks before trusting it near main. It's now part of the default flow, and reviewers spend their time on the parts that actually need judgment.

Pros
  • Fast to set up
  • Caught real bugs
Cons
  • Occasional context loss on very large diffs