How an AI agent can prepare an incident brief with evidence, scope, and next checks
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, 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 prepare an incident brief with evidence, scope, and next checks?
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.
Fix the incident window and symptom
Provide environment, services, time window, user-visible symptom, monitor or incident link, and known deploys or changes. The agent should start with the affected signal and widen scope only when evidence points to another service.
Ask it to correlate metrics, traces, logs, monitors, and events by timestamp and identifiers. It should preserve query links and note sampling or missing telemetry.
Example starter prompt
Triage Datadog incident [link] affecting [symptom] in [environment] between [times]. Start with services [list].
Build a timeline of monitor changes, errors, latency, traces, logs, dependencies, and deploy events. Cite each query, trace, or log sample. State confirmed scope, unaffected controls, hypotheses, and the next check that could confirm or reject each one. Do not change monitors or declare root cause without evidence.Compare controls
Check a healthy region, endpoint, tenant, release, or time period when possible. Controls help separate system-wide failure from a narrow cohort. Redact secrets and personal data from log excerpts, and do not copy large raw log sets into the brief.
Questions this workflow answers
What failed first in this incident, how far did it spread, and what should responders check next?
An agent can organize Datadog monitors, metrics, traces, logs, dependencies, and deploy events around one user-visible symptom. Give it the environment, services, incident window, affected route or operation, and known changes. It starts at the first confirmed divergence and widens only when trace or dependency evidence points elsewhere.
The timeline should cite every monitor transition, query, trace, log sample, release, and timestamp. Correlation becomes a hypothesis, not a root cause. A deploy before an error spike deserves investigation, but the agent should compare unaffected services, regions, tenants, releases, or time periods. Sampling, missing logs, delayed metrics, and trace gaps remain visible.
Each hypothesis needs a check that can support or reject it. That might compare a healthy trace, inspect saturation on a dependency, verify a config change, or reproduce one request. Sensitive values and personal data are redacted, and the brief links to queries instead of copying large log blocks. Mitigation ideas include blast radius, rollback, owner, and approval.
The incident lead receives scope, timeline, controls, evidence, ranked hypotheses, and next checks in order. Responders can assign investigation without rereading every dashboard. The agent does not alter monitors or declare the incident resolved. After recovery, the same evidence can support a postmortem and distinguish the confirmed cause from early theories.
The timeline should put monitors, deploys, configuration events, metric changes, trace examples, and representative logs on the same clock. For every failing service or endpoint, include a nearby healthy control where possible. That makes it easier to tell whether an error is global, release-specific, tenant-specific, or downstream. A hypothesis earns priority when it explains several observations and suggests a discriminating check; a coincident deploy alone is not enough to call the cause.
Expected handoff
Return incident scope, timeline, affected and healthy controls, evidence links, hypotheses, ordered checks, mitigation options, and unknowns. The incident lead should be able to assign the next investigation without rereading every dashboard.