AI agent blueprints for Engineering.
Bounded engineering workflows that turn operational and code evidence into reviewable action packets. Each pattern defines the evidence it reads, the artifacts it produces, the authority it may exercise, and the human who makes the consequential decision.
Platform, application, SRE, and developer-experience teams
engineering owner
private · read/propose · human-gated
Nine jobs with a crisp acceptance test.
These are implementation patterns, not generic “AI for Engineering” pages. Open one to see its exact trigger, topology, grants, approval boundary, failure modes, and KPI.
Production incident triage
Correlate an alert with deploy and service evidence, rank hypotheses, and hand an incident commander a bounded action packet.
median time to a reviewed mitigation plan
Pull request risk review
Review a proposed change against its tests, ownership boundaries, and operational risk before a maintainer merges it.
reviewed defects found before merge
Flaky test diagnosis
Cluster repeated failures, isolate timing or state signals, and propose the smallest experiment that can confirm the cause.
open flaky-test age
API contract change review
Compare an API change to the prior contract, identify affected consumers, and prepare a compatibility decision for an owner.
unannounced breaking changes
Dependency upgrade plan
Turn release notes, local usage, and test evidence into a staged upgrade plan with explicit rollback points.
upgrade lead time
Database migration readiness
Review a migration for lock, data, rollback, and rollout risks before an operator schedules it.
migration-caused incidents
Release readiness review
Assemble tests, change risk, open exceptions, and rollback evidence into one go or no-go packet.
change failure rate
Bug reproduction packet
Normalize a bug report into minimal steps, evidence, environment assumptions, and a safe reproduction brief.
time to reproducible bug
Technical runbook refresh
Compare a runbook with current service evidence and propose precise updates for an owner to approve.
runbook validation success
The workflow is autonomous. The authority is not.
- 01
Attach evidence
Put only this case’s approved inputs in a scoped workspace.
- 02
Run specialists
Each bounded node produces an artifact the next node can challenge.
- 03
Stop at proposal
External writes stay outside the agent’s default authority.
- 04
Record the decision
Keep the human approval as a decision record beside the signed evidence from the agent run.
Pick one expensive, inspectable job.
Start with real evidence, a named decision owner, and a measurable result. Expand the agent only after the receipt and acceptance test prove the workflow holds.