Incident Management: Key Best Practices with Agentic AI

Just as an airport relies on strict protocols and clear communication channels to manage emergencies swiftly and effectively, organizations depend on IT Service Management (ITSM) frameworks like ITIL incident management to handle incidents. Within the ITIL framework, incident management is a core process aimed at restoring service as quickly as possible. It is distinct from, but closely integrated with, other processes like problem and change management.

Without such a framework, incident management can resemble the frustrating experience of waiting in a long airport queue. Issues are logged, and tickets are created, but users often face long delays, sometimes waiting hours or even days for resolution. This slow incident logging and response not only frustrates users but also increases operational costs, underscoring the importance of a well-orchestrated AITSM approach.

Today’s incident management flips the script. The new goal is to “shift left” and accelerate incident resolution by moving it to the fastest, most cost-effective channels. Think of it as empowering travelers to resolve minor issues at self-service kiosks or through real-time alerts instead of always waiting for a gate agent. In IT, this means using bots, proactive notifications, and automated knowledge to resolve incidents before they ever become tickets. Only the most complex problems are escalated for hands-on attention.

By embracing this shift, organizations not only save time and money but also create a smoother, more satisfying experience for everyone involved. And with agentic AI at the helm, incident management becomes less about firefighting and more about keeping the skies clear for business to soar.

Key Differences:

In IT operations, it’s crucial to distinguish between incidents and service requests, as they require fundamentally different handling. Incidents refer to unplanned interruptions or reductions in the quality of IT services, such as a server crash, network outage, or application error, that demand immediate attention to restore normal operations.

A service request is a user-initiated request for standard services or support, such as password resets, software installations, or access to new applications. These service requests are expected, and hence service management processes are more like scheduled maintenance or upgrades, tasks that don’t disrupt business but still need to be handled efficiently.

Why Does This Distinction Matter?

Incidents demand a rapid response to minimize business impact, while service requests can often be fulfilled through standardized, automated workflows. Treating every issue as a critical incident would overwhelm IT teams and slow down resolution for everyone.

Handling Approaches

Traditional ITSM systems often route both incidents and service requests through the same ticketing process, leading to bottlenecks and delays. This is where the “shift left” strategy comes into play.

• Service requests: Automation and self-service are key. In a modern service request management system, AI agents can handle common requests instantly, freeing up IT staff for more complex work. For example, Aisera’s Task Agents can process software access requests or password resets without human intervention, reducing wait times and operational costs. A dedicated self-service portal enables employees to track, report, and resolve IT issues independently, enhancing transparency, improving user experience, and reducing incident response times by providing quick access to solutions and status updates.
• Incidents: The focus is on rapid detection, triage, and incident resolution. Here, autonomous AI systems act as an intelligent front door, using context-aware bots and proactive notifications to resolve simple incidents before a ticket is ever created. If escalation is needed, a “ticket concierge” ensures the issue is routed to the right expert, with all relevant context attached.

By clearly separating incidents from service requests and applying the right automation strategy to each, organizations can accelerate resolution, reduce costs, and deliver a better experience for users and IT teams alike.

In any IT environment, issues crop up daily, but not all issues are created equal. If you’ve ever worked in IT support, you know the difference between putting out a fire and fixing the faulty wiring that caused it in the first place. This is the heart of distinguishing incidents from problems.

Incidents are the urgent disruptions: a user can’t log in, a website goes down, or a printer stops working. The immediate goal is to restore service as quickly as possible. For example, imagine a global e-commerce company where, every Monday morning, the checkout system slows to a crawl. Support teams scramble to get things running since each slowdown is an incident, and every minute lost means lost revenue.

But why does this keep happening?

That’s where problems come in. Problems are the underlying causes behind recurring incidents. In our e-commerce example, after a few weeks of repeated slowdowns, IT digs deeper and discovers a database indexing issue triggered by Monday’s high traffic. By identifying and fixing this root cause, the team doesn’t just patch the symptoms. Rather, they permanently resolve the issue, preventing future incidents.

DevOps teams operate in a world where speed and reliability are everything. Rapid deployments, continuous integration, and constant change are the norm. But with this agility comes the challenge of managing incidents that can disrupt the flow of innovation.

Picture a software company rolling out new features every week. While this pace delights customers, it also increases the risk of unexpected bugs, performance drops, or integration failures. When incidents occur, DevOps teams need to identify, diagnose, and resolve issues fast so they can keep delivering value without interruption. To maintain service continuity and support ongoing innovation, DevOps teams must efficiently manage incidents, ensuring that disruptions are minimized and services remain reliable.

How Incident Management Evolves for DevOps

Traditional incident management processes, with their heavy reliance on manual ticketing and siloed communication, often can’t keep up with DevOps velocity. Instead, successful teams automate detection and response wherever possible. For example, when a new release causes a spike in error rates, monitoring tools can trigger alerts, and automated bots can collect logs, roll back changes, or even initiate remediation steps.

Agentic AI takes this a step further. By integrating with CI/CD pipelines, monitoring tools, and knowledge bases, Aisera can proactively detect anomalies, predict potential outages, and orchestrate incident response across development and operations teams. This means issues are flagged and addressed before they escalate, and knowledge from past incidents is seamlessly incorporated into future workflows.

Incident management is a structured, multi-step lifecycle designed to ensure that IT service disruptions are resolved efficiently and effectively. Understanding each phase of this process is critical for organizations aiming to reduce downtime and improve service reliability.

An incident management system enables organizations to quickly respond to and resolve service disruptions, improving service quality and building institutional knowledge. Incident management software streamlines the entire process, from logging to resolution, by automating workflows, enhancing visibility, and supporting integration with other IT operations tools.

Here’s a typical sequence of steps followed in a modern incident management process:”

Lifecycle Steps:

• Logging: Every incident is recorded in a centralized system, capturing comprehensive incident-related information for effective tracking and analysis.
• Categorization and Prioritization: Incidents are categorized and prioritized based on their impact and urgency. Prioritizing incidents ensures that high-priority incidents, such as those affecting system availability, security breaches, or hardware failures, are identified and addressed immediately.
• Investigation and Diagnosis: The support team investigates the root cause of the incident, often involving relevant stakeholders to ensure all necessary expertise and decision-makers are engaged in the resolution process.
• Resolution and Recovery: The issue is resolved, and services are restored to normal operation.
• Closure and Documentation: The incident is formally closed, with all actions documented. Incident closure is the final step in the workflow, ensuring proper documentation and communication.

Automation and Real-Time Monitoring

Automation is embedded throughout the incident management lifecycle to improve speed and accuracy. Real-time monitoring systems in incident management continuously feed data into AI models that detect anomalies before users report issues. This proactive detection enables early intervention, often resolving incidents before they impact business operations.

Incident response is at the heart of the incident management process, encompassing all actions taken to address and resolve incidents from the moment they are detected until they are fully closed. Following the ITIL incident management process, organizations adopt a structured approach that prioritizes the rapid restoration of normal service operation, minimizing the impact of disruptions on business activities.

The incident response process starts with initial diagnosis, where you assess the nature and scope of the incident. Incident response tools such as advanced monitoring software and automated alerting systems play a big part in detecting issues early and speeding up the response. If an incident can’t be fixed immediately, incident escalation ensures it gets routed to the right people or higher-level teams for further investigation and fixing.

Throughout the incident management process, using incident management tools and following established processes helps you fix incidents quickly. By focusing on fast fixes and effective escalation, IT teams can reduce downtime, get service back to normal, and deliver seamless service to end users.

Incident management teams are the heart of any incident management process, they respond to and resolve incidents that impact business operations. These teams are made up of IT professionals, service desk, technical specialists, and incident managers, each playing a part in the incident management workflow.

The service desk is the first point of contact; it handles incident identification, logging, and initial triage. When incidents require specialist knowledge or higher-level intervention, the incident management team will coordinate the escalation to get issues resolved as quickly as possible. Clear communication, defined roles, and a structured workflow are key to incident management teams working efficiently and delivering incident management.

Artificial intelligence is transforming incident management by automating root-cause analysis, incident categorization, prioritization, and resolution, as well as enabling predictive analytics and proactive monitoring.

• Reduced Mean Time to Resolution (MTTR): Aisera’s enterprise AI agent platform enables organizations to predict, detect, and remediate incidents faster than traditional approaches. Thanks to automated root cause analysis and proactive incident response, many enterprises have achieved up to a 70% reduction in MTTR after deploying our AI agent platform.
• Auto-Resolution at Scale: AI-powered automation delivers auto-resolution rates as high as 81% for enterprise customers, drastically reducing the volume of tickets requiring manual intervention and freeing up IT incident management teams for higher-value work.
• Cost Savings and Productivity Gains: Customers report up to 90% cost savings and a 50% increase in agent productivity by automating routine tasks, deflecting tickets, and enabling user self-service. This translates to millions in operational savings and thousands of agent hours reclaimed.
• Proactive Incident Prevention: An AIOps solution can predict major incidents and outages up to 48 hours in advance by analyzing data from ticketing systems and telemetry sources (logs, metrics, traces, and events). This proactive approach enables IT teams to address vulnerabilities before they escalate, ensuring greater system reliability and uninterrupted operations.
• Intelligent Workflow Automation: AI workflow orchestrates the entire incident management lifecycle, from detection and triage to remediation and closure, integrating seamlessly with next-gen ITSM platforms and existing enterprise systems. Artificial intelligence-powered tools automate key processes, improving response times and decision-making.
• Predictive Analytics and Root Cause Analysis: The predictive AI can combine operational and ticketing data to deliver actionable insights, predict SLA breaches, assess change risks, and pinpoint root causes, enabling faster and more accurate resolutions through artificial intelligence-driven analytics.
• Scalability and Adaptability: Aisera’s solutions are trusted by Fortune 500 companies and seamlessly integrate with leading SaaS applications, providing scalable, secure, and adaptable incident management for complex IT environments.

With these capabilities, organizations not only resolve incidents faster but also prevent them from occurring, optimize resources, and deliver a superior user experience while reducing costs and operational overhead.

IT incident management relies on a combination of robust tools and proven techniques to ensure rapid detection, efficient triage, and effective resolution of issues. Core components include:

• Monitoring and Telemetry Platforms: Tools like Nexthink, Intune, JAMF, and Aternity enable real-time asset discovery, performance tracking, and anomaly detection across hardware and software environments. These platforms help keep configurations up to date, identify issues early, and optimize asset lifecycles.
• ITSM and Ticketing Systems: Industry standard platforms like ServiceNow, Jira, and BMC Remedy provide centralized ticket management, incident management workflow automation, and SLA tracking. Incident management software streamlines incident handling, integrates with other IT operations tools, supports automation and reporting, and gives visibility and communication throughout the incident response process. They are the backbone for logging, categorizing, and routing incidents and service requests.
• Knowledge Management: Automated knowledge bases suggest relevant articles, generate new documentation from past resolutions, and enable seamless information sharing across IT teams. This accelerates issue resolution and boosts productivity.
• Change, Release, and Problem Management: Automated incident management tools assess risk, orchestrate deployment tasks, track recurring issues, and implement fixes to prevent future disruptions. These capabilities are essential for minimizing the impact of service outages and maintaining smooth operations.
• Proactive Prediction and Major Incident Detection: Advanced analytics and AI-driven systems analyze ticketing and telemetry data to predict outages and incidents up to 48 hours in advance, allowing teams to take preventive action before users are affected.

Integrating Agentic AI in Incident Management Systems

Aisera’s AI for IT enhances and unifies these tools by embedding AI agents that automate, orchestrate, and optimize every phase of incident management:

• Seamless Integrations: Aisera connects with hundreds of enterprise applications and ITSM, enabling rapid onboarding and real-time data synchronization across the tech stack. Its open API architecture ensures effortless connectivity with both commercial and homegrown tools.
• Autonomous AI Agents: Specialized domain agents handle asset management, release management, problem management, change management, and proactive incident detection. These agents autonomously execute workflows, analyze context, and resolve issues without manual intervention.
• Real-Time Orchestration: Agentic AI coordinates detection, ticket triage, remediation, and closure by leveraging real-time data and contextual knowledge. It can trigger automated actions, escalate complex cases, and update knowledge bases dynamically.
• Scalability and Security: Designed for enterprise deployments, secure, compliant, and scalable across global environments.

By adding agentic AI to incident management, organizations turn traditional incident management into a proactive, autonomous, and highly efficient process, reducing manual workload and resolution times and driving business outcomes. Post-incident analysis is also key as it allows organizations to review past incidents, identify root causes, improve processes and prevent recurrence, and improve service quality and incident management practices.

1. Proactive Incident Detection and Prediction

Utilize autonomous AI systems to continuously analyze telemetry, logs, and ticketing data in real time to predict and detect incidents before they impact users. Early identification enables IT teams to address potential issues proactively, minimizing business disruption and improving service availability.

2. Automated Root Cause Analysis and Remediation

Deploy AI-driven automation to quickly analyze incident symptoms, correlate them with historical data, and identify root causes. Automated remediation workflows can then be triggered to resolve common issues, such as service restarts or configuration adjustments, reducing mean time to resolution (MTTR) and accelerating recovery.

3. Continuous Learning and Knowledge Management

Implement closed-loop AI systems that learn from every incident and user interaction. The knowledge management systems can automatically generate and update knowledge base articles, identify knowledge gaps, and refine support processes, leading to higher rates of automated resolution and more efficient IT support over time.

4. Intelligent Change Risk Assessment and Automation

Incorporate AI into change management to assess the risk of proposed changes based on historical patterns and system dependencies. Automate approvals for low-risk changes and recommend pre-change validations to minimize unplanned downtime and enable faster, safer deployments.

5. Regular Audits, Transparency, and Compliance

Establish governance frameworks to conduct regular audits of AI-driven processes, ensure transparency in AI decision-making, and maintain compliance with industry standards such as ISO/IEC 27001, SOC 2, and GDPR. This approach builds trust, protects data privacy, and ensures responsible and secure use of AI in incident management.

As organizations seek to move beyond traditional ITSM and embrace true autonomy, unlike traditional platforms that add automation as an afterthought, Aisera was built natively with AI to support intelligent, autonomous incident management. Automation and streamlined processes can significantly enhance employee productivity by reducing downtime and empowering staff to resolve issues quickly.

Choosing Aisera means partnering with a platform designed not just to automate but also to transform incident management, turning reactive firefighting into proactive, intelligent operations. Aisera offers a uniquely powerful, AI-driven path to resilience, agility, and exceptional service delivery. Book a free AI demo to experience the power of Aisera’s agentic AI today!