An efficient incident management workflow is crucial for your business to maintain system uptime and customer trust. Manual processes, especially during high-pressure incidents, are prone to errors and delays. The integration of AI agents is now revolutionizing this space, offering a path to a truly automated incident management system.
This post will cover some of the ways you can integrate AI agents into your incident management process to improve your workflows and ultimately reduce mean time to resolution (MTTR).
Why use AI agents in major incident management?
In the response phase of a major incident, AI agents can act as "digital co-pilots" that handle the intensive orchestration and execution required to restore service. While humans make the high-level strategic decisions, AI agents eliminate the "to-do list" friction by autonomously performing high-stakes tasks such as isolating compromised systems, rolling back failed deployments, or scaling resources to absorb traffic spikes. They serve as a connective tissue across your entire stack, simultaneously updating Slack channels, drafting stakeholder briefs, and inviting the correct on-call experts based on the real-time technical context. By taking over the repetitive coordination and precision execution, which are often the first things to fail under human stress, AI agents ensure that the recovery plan is carried out with machine-like consistency, significantly lowering MTTR without increasing the cognitive load on responders.
These are some of the core functions that AI agents can perform during a response:
- Precision orchestration: Execute complex, multi-step remediation playbooks (e.g., database failovers or traffic draining) without the risk of manual typos or skipped steps.
- Live stakeholder management: Automatically translate technical progress into plain-language updates for status pages and executive channels, keeping non-technical teams informed without interrupting the engineers.
- Dynamic role assignment: Identify and "page" the specific subject matter experts needed based on the services currently failing, rather than just alerting a general on-call rotation.
- Incident "black box" logging: Record every action taken by both humans and machines in real time, ensuring the post-incident review (PIR) is ready the moment the incident is resolved.
Follow these five steps to automate your incident management workflow and introduce AI agents.
1) Analyze the existing incident management process workflow
Before implementing AI agents in your incident management workflow, you must first deconstruct your current response phase to identify "execution gaps" where manual intervention slows down recovery. Start by auditing your most frequent high-severity incidents and mapping out the specific technical tasks currently performed by responders. You are looking for high-frequency, "mechanical" actions that follow a predictable logic, which are the prime candidates for agentic execution. This way, you can define exactly where an agent has permission to act autonomously (such as scaling a cluster) versus where it must pause for a human checkpoint (like a database migration). Analyzing your workflow in this way transforms your runbooks from static documents into actionable logic maps that an AI agent can reliably navigate to restore services.
2) Identify automation opportunities in your workflow
While AI agents form one part of your incident management modernization journey, a good interim step can be identifying areas for regular automation. By analyzing your existing processes, you can pinpoint high-impact, repeatable tasks ready for automation:
- Rapidly mobilizing the appropriate responders: Use logic to automatically engage the correct on-call teams based on the incident's category and affected service.
- Task assignment and handoff: Take tasks out of chat functions and put them into an automated runbook that allows for task tracking, prioritization, and notifications.
- Status updates and stakeholder notifications: Automate communication to internal and external stakeholders, providing timely updates without requiring constant manual effort from the incident commander.
- Automatically captured audit trail: Remove the manual effort needed to piece together an event after the fact with inaccurate or incomplete data by implementing an audit trail that automatically captures tasks as they happen.
Focusing on these task-based workflows can immediately lighten the administrative load, addressing many common major incident management challenges.
3) Bring agentic AI into your incident management workflow
The next evolution involves adopting agentic AI for incident management. Unlike simple automation scripts, AI agents can perform complex reasoning, plan multi-step actions, and adapt to novel situations without explicit pre-programming.
AI agents are changing incident manager roles and responsibilities by taking over the cognitive and administrative burden of the initial response. These agents can:
- Synthesize context: They pull data from your ITSM and other tools to instantly create a concise, structured incident summary.
- Propose solutions: Based on historical data and real-time analysis, they can suggest the most likely fix or the next diagnostic step.
- Execute automated runbooks: Automated runbooks provide the framework needed for agents to autonomously execute tasks while maintaining visibility and transparency.
The best major incident management software is now leveraging this technology to deliver a faster, more consistent response.
4) Integrate human checkpoints in the automation loop
While the goal is to automate incident management, human oversight remains non-negotiable, especially for major incidents. The most robust system is a collaborative one. There are certain areas where keeping people in the loop is important to keep control of the response:
- Review and approval: For high-impact or irreversible actions (like restarting critical systems), the AI agent should propose the action and await human approval.
- Continuous feedback: Incident responders provide feedback on the AI's suggestions, which retrains and improves the agentic AI for incident management over time.
5) Define success metrics and monitor performance
Once you’ve implemented AI and automation, ROI must be rigorously measured. You can validate the effectiveness of your new incident management workflow with key metrics:
- MTTR: The single most important measure. A reduction here proves the automation is effective. Subtract the incident end timestamp from the incident detection timestamp in your ITSM or orchestration platform to find out your incident’s MTTR and track whether this is reduced after implementing AI agents.
- A reduction in human intervention: Measures the decrease in manual steps required from responders. You can calculate this by comparing the number of manual tasks completed in your response runbook vs. AI agent tasks.
Continuous monitoring is vital for refinement. If the metrics don't improve, iterate on your automation rules and workflows.
Use Cutover automated runbooks to accelerate adoption
To rapidly transition to a mature, automated model, consider adopting a platform that specializes in orchestration and human-in-the-loop automation. Cutover with automated runbooks and Cutover AI provides a visual, collaborative platform that bridges the gap between human and machine actions.
These AI automated runbooks are more than just documentation; they are live workflows that orchestrate both automated actions and manual task-based workflows. This unified approach ensures that every step of the incident management process workflow is trackable, auditable, and repeatable, dramatically accelerating response times and reducing MTTR.