AI INCIDENT PREVENTION
Predict and Prevent Incidents with AI Agents
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.
Prevent incidents by addressing risk before failure occurs
Reduce outages and their impact by acting on risk signals instead of reacting to incidents.
Key Benefits:
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Fewer repeat incidents Recurring issues are identified, tracked, and resolved at the root, reducing the same failures from resurfacing across teams and environments.
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Lower change-related risk Proposed changes are evaluated against historical outcomes and system context, helping teams adjust or delay changes that are likely to cause incidents.
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Faster problem resolution Underlying causes are surfaced earlier, allowing teams to implement permanent fixes instead of repeatedly responding to symptoms.
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More resilient operations By preventing outages before they occur, teams improve service reliability without increasing operational overhead.
90%
Less alert noiseIdentify risk and prevent repeat incidents
Predict change-related incidents
Agentic AI evaluates proposed changes using historical incident data and system behavior to surface risk before changes are deployed.
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Detect early signals linked to past change-related failures
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Assess downstream impact across services and dependencies
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Flag and prioritize changes that require intervention before deployment
Eliminate recurring issues
Patterns across incidents are analyzed to identify root causes, enabling teams to resolve problems permanently rather than repeatedly reacting.
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Recurring incident detection
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Root cause pattern analysis
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Permanent remediation tracking
Turn past incidents into prevention
Incident data is continuously analyzed and reused to inform future prevention strategies and AI recommendations.
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Link incidents to systems, changes, and dependencies over time
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Identify conditions that commonly lead to repeat failures
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Apply learned context to flag and prevent similar incidents before impact
Automate post-incident analysis
Post-incident reports are generated automatically to document causes, impact, and actions taken—creating consistent inputs for prevention.
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Automated post-mortems
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Timeline and impact summaries
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Prevention-oriented insights
Maintain operational control
Prevention workflows operate within defined policies to ensure safety, accountability, and compliance.
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Apply prevention recommendations based on approved policies and thresholds
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Record decisions, actions, and outcomes for audit and review
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Control when and how preventive actions are executed
Strategic AI partnership
Self-healing infrastructure with LogicMonitor, IBM, and Red Hat
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
Confused by Agentic AI? Us Too. Here’s the Guide We Wish We’d Had.
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AI agent for incident prevention
AI agents that prevent repeat failures
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
FAQs
Get the answers to the top agentic AIOps questions.
How does agentic AI predict IT incidents before they occur?
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.
How does a context graph help prevent change-related outages?
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.
How does agentic AI reduce repeat incidents using a context graph?
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.
What role does the context graph play in post-incident analysis?
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 moreHow does incident prevention improve operational resilience?
By 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
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