Project Name
Agentic AI Incident Auto-Resolution for Enterprise IT Operations
The client is a large enterprise technology organisation operating a distributed IT infrastructure estate across multiple regions and service domains. Their IT operations team managed a high volume of daily incidents, a disproportionate share of which were repeat occurrences of well-understood failure categories including disk fills, service crashes, connection pool exhaustion, and certificate expirations. Each had established runbooks, known resolution steps, and predictable recurrence patterns.
Despite this predictability, every incident was handled manually. Engineers logged in, executed runbooks, verified resolution, and closed tickets for events they had resolved dozens of times before. MTTR was inflated not by diagnostic complexity but by queue wait time, and on-call engineers were being woken at all hours to execute fixes that required no genuine judgement. Leadership identified autonomous incident resolution as a strategic capability that would improve service availability, reduce MTTR, and materially reduce the operational burden on engineering teams.
Ksolves, an AI-First company, built an Agentic AI system that identifies known repeat incidents the moment they occur and resolves them automatically, without human intervention, in under 2 minutes from detection to ticket closure.
The challenges faced by the client are as follows:
- High Volume of Repeat Incidents Consuming Engineer Time: A significant percentage of weekly incident volume was composed of known, documented failure types, each resolved manually despite having an established, repeatable fix that required no diagnostic effort.
- Mean Time to Resolution Inflated by Queue Delays: Even when resolution steps were fully known, incidents queued behind other work meant MTTR was inflated not by complexity but by the time taken to reach the incident in the queue.
- Runbook Execution Still Manual: Although runbooks existed for common incident types, executing them required an engineer to log in, run commands, verify resolution, and close the ticket, a sequence consuming 20 to 45 minutes per incident for work that was entirely automatable.
- On-Call Burden Creating Attrition Risk: Repeat incidents occurring outside business hours required on-call engineers to wake up and manually execute well-understood fixes, contributing to burnout and attrition risk across the operations team.
- No Learning from Repeat Incidents: Each recurrence of a known incident type was treated as a new event with no automatic pattern recognition, runbook update, or capability improvement. The system had no mechanism to improve from its own incident history.
Ksolves designed and deployed an Agentic AI system that continuously monitors the incident stream, classifies incoming incidents against a library of known resolution patterns, and autonomously executes the appropriate remediation runbook for known incident types, with human escalation reserved for novel or complex events.
- Incident Classification and Pattern Matching Engine: An AI classification layer maps incoming incidents against a curated library of known incident patterns, determining with high confidence whether an incident is a known type with an established resolution path before any action is taken.
- Agentic Runbook Executor: For classified known incidents, an autonomous agent executes the full resolution runbook including API calls, configuration changes, service restarts, and disk cleanup without human involvement, completing resolution in seconds rather than minutes.
- Confidence-Gated Human Escalation: Incidents below the classification confidence threshold are automatically escalated to engineering with a full AI-generated diagnosis brief and recommended resolution, ensuring novel incidents receive appropriate human attention without delay.
- Post-Resolution Learning Loop: Every resolved incident, both autonomous and human-handled, feeds back into the pattern library, expanding the system's autonomous resolution capability and updating runbooks to reflect any resolution variations discovered in the process.
- Audit Trail and Compliance Logging: Every autonomous action is fully logged with timestamp, classification decision, actions executed, and resolution verification, providing a complete audit trail for change management compliance.
Technology Stack
| Category | Technology |
|---|---|
| AI/ML | Agentic AI / LLM Orchestration |
| Integration | ITSM Integration (ServiceNow / Jira Service Management) |
| Platform | Runbook Automation Engine |
| AI/ML | Incident Pattern Library |
| Processing | Real-Time Incident Stream Processor |
- Near-Zero MTTR Achieved on Known Incident Types: Known repeat incidents that previously queued behind other work with MTTR of 20 to 45 minutes now complete autonomous resolution in under 2 minutes from detection to ticket closure.
- Repetitive Ops Workload Significantly Reduced: A material share of weekly engineering hours previously consumed by manually executing runbooks for predictable, repeat incident categories is now handled autonomously, freeing significant engineering capacity for higher-value work.
- On-Call Incident Volume Materially Reduced: On-call engineers previously woken for known incidents at all hours now find the majority of repeat incident types resolved autonomously overnight with no human intervention required.
- Incident Pattern Library Growing Continuously: Every resolution enriches the pattern library, continuously expanding autonomous capability over time, replacing a static process that previously treated every recurrence as a new event.
By integrating Agentic AI orchestration, automated runbook execution, real-time incident classification, and a self-improving pattern library, Ksolves transformed the client’s IT operations from a reactive, manual process into a self-healing infrastructure capability. MTTR on known incident types fell from 20 to 45 minutes to under 2 minutes, repetitive engineering workload was significantly reduced, and on-call burden decreased materially across the operations team.
The self-improving pattern library means the system’s autonomous resolution capability grows with every incident, creating a compounding improvement in operational efficiency over time. The organisation is now positioned to extend autonomous resolution to additional incident categories, integrate predictive failure detection to resolve incidents before they occur, and expand the system across additional service domains.
If your operations team is still manually resolving incidents that an AI agent could fix in seconds, Agentic AI Consulting Services from Ksolves can help you build self-healing infrastructure, eliminate repetitive ops workload, and give your engineering teams their time back.
Is Your Operations Team Still Manually Resolving Incidents That an AI Agent Could Fix in Seconds?
