Most operations teams are stuck in a reactive loop: Resolving incidents as they happen, then moving on to fight the next fire. This approach keeps things running in the short term, but prevents responders from documenting their learnings in a way that improves overall system resilience.

There are practical reasons for this. Incident response relies on human expertise, but experts are so critical to day-to-day operations that they typically don’t have the opportunity to reflect on incidents and capture what they learned. As incidents increase amid growing system complexity, translating knowledge into action gets even harder.

But things are changing. Generative and agentic AI are making it possible to capture human insight and turn it into institutional knowledge. AI is helping teams conduct more effective post-incident reviews; learnings from those reviews are then being used to streamline and automate responses to future incidents. 

This frees experts to focus on big picture, proactive work, such as improving recovery playbooks and tackling the systemic issues behind repeat incidents. By breaking the reactive loop of moving from incident to incident, systems—and the teams that manage them—become smarter and more resilient. 

What’s keeping teams in the reactive loop?

Incident response has evolved for speed, not for learning. Metrics like mean time to restore (MTTR) incentivize teams to focus their efforts on resolving incidents quickly. Responders are occupied with containment and recovery, leaving little time for documentation, analysis, or reflection.

Compounding this problem is the fact that capturing a comprehensive summary of an incident is incredibly time-consuming. Records are scattered between platforms, systems, and documents: emails, Slack threads and channels, conference calls, and notes on responders’ phones. Historically, there have been few effective options for collating this information in a way that is easily referenceable or repeatable.

Without dedicated resources or streamlined tools, lessons from incidents rarely translate into institutional knowledge. Blind spots develop: Teams end up responding to essentially the same incidents over and over because they don’t have time to identify or address the root causes. The burden of response falls on the few people who know the system best. Over time, this imbalance drains expertise and increases the risk of staff burnout.

How scale compounds complexity and risk

Organizations may not notice the limitations of this approach until they expand their tech stack. New tech introduces more complex dependencies; the rapidly expanding marketplace of AI solutions further complicates incident response.

As complexity grows, individual responders’ intuition and experience become increasingly fragmented. Different people know the ins and outs of different parts of their system, but it’s harder for any one person to have a universal understanding of how everything interacts. 

When something goes wrong, responders—even those who know their part of the system well—struggle to gather important context. Incidents become more resource-intensive and take longer to resolve; key employees burn out faster. And when things can’t be resolved efficiently, or promised remediations aren’t delivered on, customers and stakeholders become frustrated, and the brand’s reputation suffers. 

From incident response to institutional learning

Our approach applies AI across the full incident lifecycle. From the initial response to the post-incident review, PagerDuty’s AI agents surface what’s important, automate manual tasks, accelerate the path to resolution, and build a system that learns from every incident.

When a new incident emerges, SRE Agent draws on what’s happened before—past incidents, recent changes, dependency relationships, and critically, how your team has successfully resolved similar issues. This memory sharpens response by surfacing patterns across services, connecting current symptoms to previous fixes, and recommending actions based on what’s worked. Teams resolve incidents faster with fewer people pulled in, reducing the burden on on-call responders.

Meanwhile, Scribe Agent is capturing logs, alerts, and meeting transcripts during the incident. By the time the post-incident review begins, every action and decision has already been documented. What was once a time-consuming manual process now generates a structured narrative and democratizes institutional knowledge instantaneously.

Each incident adds to the system’s understanding. Insights Agent takes intelligence gathered during the response and uses it to inform strategy for future incidents. Over time, the organization builds a living knowledge base that continuously refines and improves its decision-making. The system learns, adapts, and gets smarter with every incident.

Continuous learning is the new operational standard

With AI-first operations, organizations move beyond merely surviving incidents: They learn from them and become stronger. Tools like PagerDuty’s SRE Agent, Scribe Agent, and Insights Agent turn every incident into an opportunity. The result is a more resilient, less reactive organization—one that truly gets smarter with every incident.

Learn more about how PagerDuty’s agents are transforming incident response.