Engineering
AI Engineering Incident Retrospective Agent: turn scattered evidence into reviewed action
Reconcile code, error, and responder evidence into a source-linked chronology and remediation brief without letting AI decide causality.
What changes
Evidence gathering
Owners search several systems and copy partial context
ZeroTwo assembles dated source evidence into one review packet
Decision quality
A score or summary hides missing and conflicting context
The recommendation shows sources, confidence, exceptions, and limitations
Approval
Actions move forward through informal messages
A named owner approves consequential actions before execution
Follow-through
The decision and rationale disappear after the meeting
The approved outcome, owner, and next review remain attached
Why teams use ZeroTwo for Incident retrospective preparation
Faster evidence assembly
Start from a structured source packet instead of rebuilding the same context for every review.
Reviewable recommendations
See evidence, missing fields, confidence, and limitations before accepting a suggested action.
Visible ownership
Route exceptions and consequential changes to the person accountable for the decision.
Reusable operation
Save the schema, prompt, approval rule, and review date for the next run.
Incident retrospective preparation fails when evidence and ownership stay fragmented
Engineering Management, Site Reliability Engineering, Incident Response often need information from github, sentry, slack. When each owner sees only one system, the decision can look complete while dates, exceptions, or required evidence remain missing.
Manual assembly also makes the review hard to repeat. A recommendation may be correct, but the next operator cannot see which source supported it, which assumption changed, or who approved the action. The agent should reduce collection work without hiding those boundaries.
How ZeroTwo runs the Incident retrospective preparation workflow
Collect current source evidence
GitHubCollect current source evidence using dated evidence, explicit decision fields, confidence notes, and a named owner so the result remains inspectable before any consequential action.
Normalize timestamps and incident identity
SentryAlign event times, deploy references, issue IDs, and affected services while preserving original values and explicit uncertainty.
Reconcile responder evidence
SlackCompare alerts, issue comments, operator messages, and code changes, keeping gaps and contradictory causal claims visible.
Draft the retrospective
RefactorPrepare the chronology, impact, contributing-condition hypotheses, unresolved questions, and proposed remediation items with source links.
Route approval to the accountable owner
SlackRoute approval to the accountable owner using dated evidence, explicit decision fields, confidence notes, and a named owner so the result remains inspectable before any consequential action.
Record the decision and next review
GitHubRecord the decision and next review using dated evidence, explicit decision fields, confidence notes, and a named owner so the result remains inspectable before any consequential action.
The agent prepares evidence; the accountable owner makes the decision
ZeroTwo treats GitHub, Sentry, Slack, and the review skill as inputs for one job: reconstruct the incident from primary evidence and prepare a defensible retrospective. The workflow stays centered on chronology, contributing conditions, and remediation ownership rather than becoming a generic connector recipe.
The boundary matters. The agent should not invent missing evidence, treat a score as certainty, or execute a sensitive update without approval. Conflicting and stale inputs remain visible so the owner can revise, defer, or stop the recommendation.
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Describe what you need
“Before every Incident retrospective preparation review, gather current evidence from github, sentry, slack, identify missing or conflicting context, draft a recommendation with confidence and limitations, and route it to the accountable owner for approval.”
It runs on schedule
Runs on the review cadence with event-triggered refreshes when a material source changes.
Frequently asked questions
An AI incident retrospective agent prepares a reviewed decision packet for Engineering Management, Site Reliability Engineering, Incident Response. It gathers source evidence, normalizes the required fields, identifies gaps, and proposes next actions while keeping consequential changes behind human approval.
This workflow uses github, sentry, slack. Teams can add other approved sources when they materially affect the decision, but each added connector should have a clear purpose, permission boundary, and accountable owner.
ZeroTwo can automate approved low-risk steps, but sensitive messages, access changes, customer commitments, and material record updates should keep a human approval gate. Missing or conflicting evidence should stop the action rather than produce a more confident draft.
Track evidence completeness, reviewer correction rate, time to approval, reopened decisions, and downstream errors. The goal is faster reviewed action with fewer unsupported claims, not the largest number of automatic updates.
The workflow should preserve its source packet, current stage, proposed action, and owner so a person can resume manually. Recovery behavior belongs in the initial test because a useful agent must fail visibly without losing decision context.
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Turn Incident retrospective preparation into reviewed action.
Connect github, sentry, slack. ZeroTwo prepares the evidence and keeps owners in control.