Engineering
AI incident response automation that runs the first ten minutes of an incident - gathering evidence, forming a probable cause and opening the channel - so the human who gets paged starts from a briefing rather than a blank screen.
What AI incident response automation does
AI incident response automation is a scoped autonomous worker that handles the mechanical opening of an incident: claiming the alert, correlating signals, assembling context, opening the channel and paging the right person with a briefing attached.
It does not decide severity for you and it does not fix the underlying fault. Its job is the ten minutes that are identical in every incident and that nobody should be doing at 3 a.m.: finding what changed, checking whether a dependency is degraded, pulling the last deploy, seeing whether this looks like something that has happened before, and writing it down.
The measure is how a responder starts. Opening a laptop to a channel that already contains the timeline, the recent changes and a probable cause is a materially different job from opening it to a red graph.
Inputs -> Outputs
| It reads | It produces |
|---|---|
| Alerts and monitoring signals | A claimed incident with a severity recommendation |
| Recent deploys and configuration changes | A “what changed” summary covering the relevant window |
| Dependency and third-party status | A note when the cause is upstream and not yours |
| Logs and traces around the failure | A probable cause with the evidence behind it |
| Past incidents and their resolutions | A link to the closest prior incident and what fixed it |
| Your escalation policy | The right person paged, with the briefing attached |
Where it runs
- Alerting and on-call platforms
- Observability, logging and tracing
- Incident management tooling
- Chat, for the incident channel
- Cloud platforms and orchestrators
- Status pages
- CI/CD, for recent changes
Platform names are shown as illustrative examples of a category, never a claim of a delivered integration.
A day in its life
| Time | What it does |
|---|---|
| 03:22 | Checkout error rate crosses threshold. The agent claims the alert within seconds. |
| 03:22 | It opens an incident channel and starts a timeline that will become the post-incident record. |
| 03:23 | It correlates: a deploy went out at 03:04, and a payment provider reports degraded status. |
| 03:24 | Probable cause: upstream provider, not the deploy. It says so, with both pieces of evidence. |
| 03:25 | Severity clears the threshold for a page. It pages the on-call engineer with the briefing. |
| 03:31 | The engineer confirms upstream, posts a status update, and the agent keeps the timeline running. |
Guardrails and human-in-the-loop
Autonomy boundary
It may claim alerts, gather evidence, open channels, recommend a severity and page according to your policy. It may not change infrastructure, restart services, or close an incident.
Approval gates
Declaring a major incident, posting to a public status page and any customer communication all require a person.
What stays human
Judgment about customer impact, the decision to escalate beyond the on-call rota, and anything requiring a commercial or legal call.
Escalation
It pages on your policy, and if the first responder does not acknowledge within your window it escalates rather than waiting.
Logging
The full timeline is the audit trail and the first draft of the post-incident review, which is the document everyone intends to write and rarely does.
The human role it augments
This agent does not replace your on-call engineer. It removes the first ten minutes of every incident - the gathering, the correlating, the channel admin - so the engineer’s attention goes to diagnosis and the fix rather than to assembling context under time pressure at an hour when nobody thinks clearly.
It also improves the incidents nobody remembers. The timeline is captured while it is happening rather than reconstructed days later from chat scrollback, which is what makes a post-incident review honest.
Time to value and cost shape
- Cost shape - Priced per incident rather than per engineer seat. The comparison is minutes of responder time at an unsociable hour, plus the customer-facing cost of a longer time to diagnosis.
- Model your own figures - ROI calculator · what a hive costs
KPIs it moves
- Time to context
- Alert to a responder having a briefing, before and after (Yours)
- Correlation accuracy
- Share of probable causes confirmed by the responder (Target)
- Post-incident coverage
- Share of incidents with a complete timeline captured live (Target)
- 100%
- Of incidents carrying an auditable, timestamped record (Target)
Provenance is shown on every cell. Nothing here is a client outcome.
Frequently asked questions
Does it fix the incident?
No. This agent runs triage and briefing. Remediation is the Self-Healing Infra Agent, which acts only within a set of fixes you have pre-approved. Separating them is deliberate: diagnosis and action carry different risks and deserve different boundaries.
Will it wake people unnecessarily?
It pages on your existing policy rather than inventing one, and it starts in observe-only mode so you can see what it would have done. In practice the correlation reduces unnecessary pages, because it can tell that an alert is an upstream provider issue before a human is woken to work that out.
What if its probable cause is wrong?
It is a recommendation with the evidence attached, not a verdict, and responders override it freely. Correlation accuracy is tracked, so a persistently wrong agent is visible as a number rather than as a gradual loss of trust. Getting the "what changed" summary right is valuable even when the cause is wrong.
Does it need write access to production?
No, and that is the point of separating it from remediation. It needs read access to alerts, logs, traces, deploy history and dependency status, plus permission to open a channel and page. Its credentials are scoped to exactly that.
Related agents
Self-Healing Infra Agent
Applies the approved fix once the cause is known.
Dependency & Patch Agent
Removes a recurring class of incident cause.
Build & Release Agent
Rolls back the release that started it.
Code Review Agent
Catches some of these before they ship.
Test-Writing Agent
Turns each incident into a regression test.
Competitor Watch Agent
A different department, same pattern: watch, correlate, report.
