Operations | Monitoring | ITSM | DevOps | Cloud

IT problem management VS. IT incident management, and how agentic ITOps improves both

Picture a familiar scene: a critical application goes down during peak business hours, and your on-call engineers scramble to restore service. Two weeks later, the same application fails again, frustrating your teams with the same symptoms, the same scramble, and the same customer frustration. If this pattern feels familiar, your organization may be strong at IT incident management, but underinvested in IT problem management.

From AIOps to agentic ITOps: Why AI for IT operations has entered a new era

Enterprise IT has reached an inflection point. Your teams are responsible for hybrid cloud infrastructure, microservices, third-party dependencies, and shipping AI-generated code at unprecedented velocity. IT environments are becoming more complex faster than traditional tools and processes can keep pace. Alert volumes keep climbing. Institutional knowledge keeps walking out the door. And the pressure to do more with flat or shrinking budgets isn’t letting up.

What is MTTR, and how can agentic ITOps reduce it?

Mean time to resolution (MTTR) measures the average duration to restore regular operation for an application, service, or infrastructure component. It’s a key performance indicator (KPI) for IT incident management. To tie MTTR directly to customer satisfaction, you first need to understand how it affects service and application reliability and availability. From there, you can make informed decisions, operate efficiently, and provide a seamless customer experience.

How to lay the data foundation to support agentic ITOps

Agentic IT operations have arrived. It’s no longer a question of if enterprise IT departments will adopt agentic ITOps, but how quickly. Every year, IT environments grow more distributed, complex, and difficult to monitor with legacy tools and processes. At the same time, the pace of AI development is accelerating the volume of changes and incidents, straining teams that are still trying to manage them manually, reactively, and one alert at a time.

Stop Triaging in the Dark: Full Visibility Across Every IT Domain

Alert correlation solved the noise problem. But noise was never the whole problem. Today’s most disruptive incidents cascade across networks, infrastructure, applications, and services simultaneously, without clear visibility into the true root cause. As a result, L1 teams are left manually piecing together context from multiple dashboards and tools to find the primary root cause while SLA clocks keep ticking and end user tickets add up.

Introducing the BigPanda AI Incident Assistant

AI incident assistant from BigPanda gives L2, L3, and SRE teams instant answers to resolve incidents faster without manual triage or tool-switching. IT teams lose critical minutes during incidents because context is scattered across Slack threads, bridge calls, monitoring tools, and historical tickets. The BigPanda AI Incident Assistant fixes that by surfacing relevant knowledge exactly when and where responders need it. It gives responders evidence-based resolution paths drawn from historical incidents and live system data, without leaving your workflows.

Introducing AI Incident Prevention from BigPanda

AI Incident Prevention from BigPanda stops change-related outages before they occur by leveraging risk scores, trend analysis, and guided remediation steps. Manual IT changes are still a leading cause of IT outages and disruptions. BigPanda AI Incident Prevention addresses this by automatically scoring change requests against historical data, flagging high-risk changes before they go live, and surfacing the recurring problems that cause service degradation.

6 use cases for agentic AI in major IT incident management

Enterprise IT operations leaders are realizing that legacy incident management processes cannot keep pace with today’s sprawling, hybrid-cloud enterprise environments. Enterprise IT doesn’t look anything like it did even five years ago. Hybrid cloud architectures, distributed microservices, and increasingly rapid CI/CD cycles have increased the speed and complexity of IT operations by orders of magnitude, leaving ITOps teams struggling to keep up.