How an AI agent can turn production evidence into a scoped engineering work queue
This workflow gives an AI agent a defined job, a bounded set of records, and a result a person can review. The agent reads the relevant Datadog context and matches it with Linear, applies the rules in the prompt, and keeps the source behind every recommendation. It returns a proposed handoff rather than taking consequential actions on its own.
Can an AI agent turn production evidence into a scoped engineering work queue?
Yes. Start with the scope, date range, decision rules, and fields that identify the right records. The agent can collect the evidence, compare states or sources, mark conflicts and missing data, and organize the result around the outcome above. A reviewer then checks the matches and judgment calls before approving messages, record updates, bookings, purchases, publishing, or other write actions. The guide below shows the records, boundaries, prompt, and handoff needed for this specific workflow.
Split follow-up by outcome
Datadog supplies monitors, queries, traces, logs, and incident evidence. Linear supplies scope, owners, dependencies, and completion criteria. The agent should search for existing work, then separate immediate mitigation, product or infrastructure fix, telemetry gap, and prevention task.
Example starter prompt
Use approved Datadog incident brief [link] to prepare Linear follow-up for [team/project]. Search for existing issues matching the service, symptom, and confirmed cause or gap.
For each proposed issue, include the production evidence, user or operational impact, scoped change, out-of-scope work, owner suggestion, dependencies, and measurable acceptance and monitoring checks. Link Datadog queries without copying sensitive logs. Do not create issues.Write acceptance checks against production behavior
“Fix the service” is not measurable. Define the error, latency, saturation, or alert behavior that should change, plus a regression test or safe rollout check. If root cause remains uncertain, create an investigation task rather than encoding a hypothesis as implementation scope.
Questions this workflow answers
How do we turn incident evidence into engineering work that fixes the problem and proves it stays fixed?
An agent can split an approved Datadog incident brief into distinct outcomes before drafting Linear issues. It searches for existing work using service, symptom, error, monitor, and confirmed cause or telemetry gap. Then it separates immediate mitigation, product or infrastructure fix, missing observability, cleanup, and prevention. Different owners and acceptance checks often make those separate issues.
Each issue carries the minimum useful production evidence: affected service and window, user or operational impact, query or trace links, confirmed divergence, and relevant release. Sensitive logs remain in Datadog. If cause is still uncertain, the scope is an investigation with a decision criterion, not a guessed code change.
Acceptance checks describe observable behavior. Error rate should remain below a stated threshold for the affected path; latency should return to a defined range; a regression test should cover the failure; a monitor should detect the condition without duplicate noise. Rollout and rollback checks belong in the issue when the change affects production risk.
Incident and engineering leads review duplicates, scope, owner, priority, and dependencies before creation. The final queue keeps recovery separate from prevention and preserves links to the evidence that justified the work. Closing a task requires the code or configuration result plus monitoring evidence, not merely that a change shipped.
Each proposed issue should say which incident observation it addresses and how production evidence will show success. A recovery item might restore a failed dependency or change a timeout; a prevention item might add a guard, alert, load test, or runbook. Combining them can hide an urgent fix inside a broad reliability project, so the agent drafts separate scopes when the owners or acceptance checks differ. Existing Linear work is searched by service, symptom, error, and incident link before anything new is proposed.
Expected handoff
Return duplicate search results and a queue of Linear-ready issues with evidence, impact, scope, acceptance checks, monitoring, and owner. Incident and engineering leads approve which items are created and how they are prioritized.