L2, L3, and SRE Productivity
Agentic AI automates execution, diagnosis, and follow-up work across the incident lifecycle, helping L2, L3, and SRE teams resolve complex issues faster and reduce repeat incidents without adding operational load.
When ITOps automation handles execution, senior teams focus on reliability.
Key Benefits:
90%
Less alert noiseAI agents analyze incident history, telemetry, and recent changes to surface likely causes and affected systems faster.
Runbooks are executed automatically to apply fixes consistently and safely, reducing reliance on manual intervention.
Automation identifies recurring issues and applies learnings to prevent the same failures from reoccurring.
Incident data is synthesized into clear reports and reusable automation, improving future response and prevention.
Automation runs within defined guardrails, ensuring safety, compliance, and accountability.
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.
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
AI agent for L2, L3, and SRE productivity
Edwin AI deploys agents that diagnose issues, execute runbooks, and capture operational learnings automatically. This reduces repeat work for L2, L3, and SRE teams while improving resolution speed and system resilience.
67%
ITSM incident reduction
88%
noise reduction
Trusted by customers and industry leaders
GET ANSWERS
Get the answers to the top ITOps automation questions.
Automation removes execution and coordination work that typically consumes senior engineers’ time. AI agents handle diagnosis, runbook execution, and follow-up tasks automatically, so L2 and L3 teams engage later and with full context. The outcome is fewer interruptions, shorter investigations, and more time spent on complex problem-solving rather than operational cleanup.
Automation handles repeatable remediation, incident coordination, and post-incident documentation. AI agents execute approved runbooks, validate outcomes, and capture learnings without requiring SREs to step in for every incident. The result is reduced toil and more capacity for reliability engineering, performance tuning, and long-term system improvements.
Automation captures patterns across incidents, changes, and past resolutions, then applies those learnings automatically. When similar issues occur, AI agents reuse proven fixes or recommend permanent remediation paths. Over time, this reduces recurring incidents and prevents the same failures from consuming senior engineering cycles again.
Runbooks define how fixes should be applied safely and consistently. AI agents select and execute approved runbooks automatically, and can generate new ones when gaps exist, then store successful outcomes for reuse. This turns individual expertise into shared, scalable automation that improves response speed and consistency across teams.
Learn moreNo. Automation replaces repetitive execution and coordination work, not engineering judgment. By absorbing operational load, AI agents allow senior engineers to focus on architecture, reliability, and prevention. Teams become more effective without increasing headcount or burnout.
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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.
