AI INCIDENT PREVENTION
Agentic AI analyzes change activity, historical incidents, and operational patterns to identify risk early and prevent repeat outages. Reduce disruption, avoid costly rollbacks, and improve resilience across your IT environment.
Reduce outages and their impact by acting on risk signals instead of reacting to incidents.
Key Benefits:
90%
Less alert noiseAgentic AI evaluates proposed changes using historical incident data and system behavior to surface risk before changes are deployed.
Patterns across incidents are analyzed to identify root causes, enabling teams to resolve problems permanently rather than repeatedly reacting.
Incident data is continuously analyzed and reused to inform future prevention strategies and AI recommendations.
Post-incident reports are generated automatically to document causes, impact, and actions taken—creating consistent inputs for prevention.
Prevention workflows operate within defined policies to ensure safety, accountability, and compliance.
Strategic AI partnership
LogicMonitor’s collaboration with IBM watsonx and Red Hat Ansible integrates AI-driven diagnosis, code generation, and enterprise-grade automation to deliver closed-loop incident response. From detection to resolution, the system prevents outages, shortens MTTR, and scales automation across hybrid environments.
85%
faster incident resolution
Guide
See how agentic AI is actually used to automate L1 work today—from triggering runbooks to executing fixes—and where automation is proven versus still evolving.
AI agent for incident prevention
Edwin AI analyzes incident history, change activity, and operational signals to identify risk early and prevent outages before response is required. By learning from every incident and recording details in a context graph, AI agents continuously improve prevention recommendations and reduce repeat failures.
67%
ITSM incident reduction
88%
noise reduction
Trusted by customers and industry leaders
GET ANSWERS
Get the answers to the top agentic AIOps questions.
Agentic AI uses a context graph that connects incidents, changes, dependencies, and system behavior over time. By analyzing patterns in that connected data, teams can identify risk early and prevent incidents before services are affected.
The context graph shows how proposed changes relate to past incidents, affected systems, and dependencies. This allows teams to spot high-risk changes before deployment and avoid outages and rollbacks.
The context graph links recurring incidents to shared root causes and conditions across systems. Teams can address those root causes directly, reducing the likelihood that the same incident happens again.
The context graph captures what happened, what was impacted, and how systems were connected during an incident. Those relationships are reused to improve future risk detection and prevention decisions.
Learn moreBy maintaining a continuously updated view of system relationships and failure patterns, the context graph helps teams prevent incidents proactively. Over time, this leads to fewer outages and more predictable operations.
Edwin AI Resources
Legacy IT approaches can’t keep pace with today’s demands. Dive into expert guidance on applying agentic AIOps to reduce noise, accelerate resolution, and take control of complex environments.
