Investigation and response intelligence
Give every incident a focused first read.
Incident AI connects symptoms, recent changes, ownership, and runbooks into a concise investigation starting point. Evidence stays separate from generated hypotheses so responders can review the reasoning.
Incident AI Agent is one of nine specialist agents in O2 AI, the agentic operations platform from OrbOps AI.
Reviewable hypothesis
Recent configuration change may explain the error pattern.
state: ready for responder review
First read
Start with an investigation map, not another dashboard.
The agent assembles the operational evidence most responders look for first and proposes a next investigation step. It shows how the evidence relates, where uncertainty remains, and which action would require explicit production approval.
01Evidence view
Separate observed signals from generated interpretation.
02Investigation path
Connect likely causes to supporting operational context.
03Decision notes
Keep hypotheses, approvals, and outcomes readable.
Evidence stage 1
Collect
Bring symptoms, changes, ownership, and runbooks together.
Evidence stage 2
Relate
Show which evidence supports a possible investigation path.
Evidence stage 3
Prepare
Suggest the next safe diagnostic action and record the reasoning.
hypothesis requires responder validation
Incident management FAQ
Questions about evidence, hypotheses, and response control.
How Incident AI prepares a focused first read while keeping generated reasoning separate from observed signals.
What is an AI incident management agent?
An AI incident management agent brings symptoms, recent changes, service ownership, dependencies, logs, and runbooks into a focused investigation starting point. Incident AI prepares the evidence and a possible next step so responders spend less time assembling context. It does not turn a generated hypothesis into an established cause.
How does it separate evidence from an AI-generated hypothesis?
Observed signals are labelled separately from generated interpretation. Incident AI can show which deployments, errors, dependencies, or runbook steps support a possible investigation path and where evidence is missing. Responders can review that reasoning, reject it, or request another diagnostic step before acting on the hypothesis.
Can it identify whether a deployment caused an incident?
The agent can connect incident timing and symptoms with recent deployment records and affected dependencies. That relationship is evidence for investigation, not proof of causality. Responders validate the suspected link through logs, tests, rollback evidence, or other diagnostics before deciding whether a release should be changed or reversed.
Can the incident agent remediate production autonomously?
Incident AI prepares diagnostic or remediation options with supporting evidence and expected verification steps. Production changes remain controlled by authorized responders and the configured policy gate. This boundary allows the agent to reduce investigation overhead while keeping high-impact decisions, exceptions, and uncertain actions under accountable human control.
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