Transforming the Incident Lifecycle With AI Agents

We're in the midst of a fundamental shift in how organizations run operations. 51% of companies have already deployed AI agents . What was once reactive and manual is becoming intelligent, automated, and AI-driven. The organizations that embrace this shift gain more than just operational efficiency; they develop a strategic competitive advantage that directly impacts business outcomes.

AI agents have a simple premise: they help people work better, faster, and smarter. These agents won't replace humans-they will augment human capabilities and allow operations professionals to move up the value chain. While AI takes over well-understood operations that historically consumed disproportionate time and attention, humans will spend more time on novel operations or creative work that drives greater business value.

At PagerDuty, we've witnessed firsthand how the right applications of AI can transform operations from a cost center to a strategic asset. Here, we will explore how AI agents fundamentally change the incident lifecycle, the measurable business impact they deliver, and the best implementation strategies for success.

The evolution of operations: From pagers to agents

Operations always revolve around this lifecycle:

• Discover potential issues through intelligent signal detection that cuts through the noise of complex systems.
• Triage the severity and business impact of incidents, enabling prioritization based on service dependencies and customer experience.
• Mobilize the right responders with the proper context at the right time, ensuring efficient team coordination during critical events.
• Diagnose root causes through data-driven and AI-surfaced analysis, shortening investigation time and accelerating path to resolution.
• Resolve the appropriate fix through automated remediation or guided human intervention to restore service and minimize impact.

But how teams do this is constantly changing. Mobilization used to happen through pagers; across surfaces, from Slack to your mobile application. Diagnosis used to occur through logs; now, it happens through real-time analytics surfaced via AI. Resolution used to happen through hands-on intervention; now, it happens through automated runbooks.

At PagerDuty, AI has been foundational to our platform for years. Initially, machine learning algorithms were designed to reduce alert noise and automatically group related incidents. More recently, we've been using generative AI to provide incident summaries, suggest remediation steps, and help teams communicate status updates.

Now, AI agents have arrived. AI agents are autonomous digital workers that go beyond chatbots and traditional generative AI by taking action to achieve specific goals in your operations . Unlike chatbots that simply respond to queries or GenAI tools that generate content based on prompts, AI agents can independently execute workflows, make decisions, and accomplish tasks that previously required human intervention.

According to the PagerDuty 2025 State of Digital Operations report , 38% of leaders expect AI agents to be core to their operations within 1-2 years, and 88% expect usage to be either core or peripheral.

The rapid adoption reflects the immediate value these agents deliver in terms of operational efficiency and reliability.

AI agents transform the operations lifecycle without eliminating its fundamental structure. The discovery-to-resolution process remains essential, but agents make each stage more efficient because, like humans, they are always learning, communicating, and acting .

Agents can:

• Continuously learn from operational data . With their ability to apply feedback information into their model, they improve over time without explicit reprogramming, learning which response strategies work best for specific incident types and adapting to your organization's unique operational patterns.
• Communicate insights across teams . Through integration with collaboration platforms, they maintain context across handoffs, translate issues into business impact summaries, and ensure all stakeholders access the same real-time information regardless of location or time zone.
• Take appropriate actions based on established playbooks and patterns . Unlike static automation, agents can make contextual decisions, choosing the right action from multiple options based on current conditions and historical outcomes rather than following rigid if-then logic.

The true power of AI agents lies in creating a collaborative partnership where:

• Humans focus on strategic decision-making, novel problems, and creative solutions.
• AI agents handle repetitive tasks, routine incidents, and data-intensive analysis.
• Teams achieve more together than either could accomplish alone.

This approach ensures that AI augments human capabilities rather than replacing them. It allows your most valuable resources-your people-to think and innovate while AI handles the predictable aspects of operations.

Understanding AI operations

AI Operations refers to how AI agents will work within the incident lifecycle. Instead of letting agents loose on every issue, we use a three-tier framework to help organizations understand what can be automated and where humans remain essential.

Tier 1: Well-understood issues (~100% AI & automation)

These are incidents where the fix is identified and easily automated. The team doesn't need to see anything else about this incident besides an AI-generated summary and, perhaps, AI-crafted insights on how to resolve the issue further upstream.

These types of incidents might include:

• A database cluster reaching 80% capacity triggers an automated scaling workflow that provisions additional resources without human intervention.
• A memory leak in a specific microservice identified previously triggers an automated restart sequence, with the AI agent performing health checks before and after to ensure proper recovery.
• When SSL certificate expiration warnings appear, the AI agent automatically initiates the renewal process, validates the new certificate deployment, and updates the documentation.

The result you want here is for the issues to be resolved automatically without waking anyone up. Ideally, all a human should see of this is an AI agent-generated after-incident report.

Tier 2: Partially understood issues (AI & automation-led + responder-assisted)

These incidents have been seen before, but might have multiple possible solutions. You need human judgment, but AI can significantly streamline the process.

Examples include:

• When a payment gateway experiences intermittent failures, AI identifies three potential resolutions and recommends the most likely fix for human approval.
• An API throttling issue where an agent provides context on recent code deployments, traffic patterns, and potential remediation options.
• Customer-reported application slowdowns where the agent correlates multiple system metrics to narrow down likely causes.

The goal is faster resolution with less cognitive load on responders. AI does the heavy lifting of data gathering and analysis, while humans make the critical decisions.

Tier 3: New and novel issues (responder-led + AI & automation-assisted)

These are unprecedented or highly complex incidents requiring human expertise and creativity. The AI's role is supportive rather than directive.

Such incidents include:

• During a never-before-seen API integration failure, responders lead the investigation while AI agents gather context, suggest diagnostic approaches, and document findings in real time.
• Zero-day security vulnerabilities where AI helps assess impact across systems while humans develop containment strategies.
• Complex service degradations spanning multiple systems where AI maintains a comprehensive timeline as humans coordinate cross-team troubleshooting.

The outcome is responders who can focus on problem-solving rather than administrative tasks. AI handles documentation, communication, and information gathering while humans apply their unique expertise to novel challenges.

How might this look in action? PagerDuty is launching three new agents that will help execute this work. These include:

• Site reliability engineer (SRE) agent : Autonomously monitors systems, identifies potential issues, and executes predetermined workflows to maintain service reliability.
• Insights agent : Processes operational data, identifies patterns, and produces actionable insights that inform strategic decision-making.
• Shift agent : Optimizes on-call schedules, manages shift coverage requests, and eliminates the manual coordination that consumes valuable engineering time.

Let's say you run an e-commerce site. A security breach takes down a top competitor, so your team prioritizes operational resilience. When a suspicious login attempt is detected, your SRE agent automatically groups the alerts to minimize noise and runs a script to check for data leakage. The incident never escalates to a human responder, preventing business impact, but an AI summary is created for the security team to review when they're back online.

Then, during your big seasonal sale, the checkout experience team sees a new incident-the system is struggling to process new orders. Diagnostics show CPU consumption spiking. Your AI agent:

• Catches all incident responders up with an AI-generated summary
• Identifies that a new payment gateway deployed 24 hours ago is likely causing the issue
• Recommends scaling the database cluster

After approval, the automation runs and resolves the incident, protecting revenue during your most critical sales period. After the incident, the AI-generated summary is ported directly into a narrative builder for post-incident review, helping your team learn and implement preventative measures for the future.

The technical foundation for all of this is the PagerDuty Operations Cloud.

With PagerDuty's 10+ years of AI innovation and proprietary data model powering the Operations Cloud, we can leverage the 18 million workflows executed, 86 billion events ingested, and 828 million incidents created in just the past year to build better agents, automate more workflows, and, ultimately, free more humans.

The business impact of AI on operations

Organizations implementing AI in operations aren't just achieving theoretical benefits-they're seeing measurable improvements across efficiency, customer experience, and innovation. The data tells a compelling story about how AI is transforming operations from a cost center to a competitive advantage.

The PagerDuty 2025 State of Digital Operations report shows that organizations leveraging generative AI in their operations report significant benefits: 38% cite higher-quality data insights, 37% increased operational efficiency, 36% improved customer experiences, and 33% improved team collaboration.

The adoption is happening across multiple operational domains, with security (41%) and DevOps automation (41%) the top use cases, followed closely by customer experience (38%), operating AI agents (37%), and incident management (34%).

These use cases reflect the versatility of AI across the operational spectrum. What's remarkable is the accelerating timeline from experimentation to implementation. Just two years ago, most organizations were still evaluating whether AI had a place in their operations. Today, the experimental phase is over-AI in operations has proven its value, and implementation is now the priority.

The competitive implications are significant. Companies with mature, AI-powered operations consistently outperform competitors in three critical areas:

1. Product velocity : They ship better products faster because their teams aren't bogged down with operational overhead.
2. Customer experience : They resolve incidents before customers notice them, and when incidents do impact customers, resolution happens in minutes rather than hours.
3. Talent acquisition and retention : Top engineers want to solve interesting problems, not babysit systems. Organizations that use AI to eliminate operational drudgery become talent magnets.

The financial outcomes follow naturally. The ROI becomes clear when operations shift from a cost center that simply "keeps the lights on" to a competitive advantage that drives business growth. This isn't just about doing more with less. It's about doing more valuable work by letting AI handle the predictable while humans focus on the novel and creative challenges that drive business forward. It's a fundamental recalibration of what operations can and should deliver to the organization.

Implementing AI operations

You should start running your operations on AI and automation today. But we'd be remiss if we didn't also highlight the challenges to AI and automation. Successful implementation requires addressing security concerns, developing skills, identifying high-value use cases, and managing change-all while maintaining compliance and building trust. Organizations face a clear set of challenges when adopting AI and automation in their operations, with recent data highlighting the primary concerns.

Data security heads the list (35%), followed by skills development (31%), identifying high-value use cases (30%), budget considerations (29%), and employee anxiety (28%).

These are more than implementation hurdles. They're strategic considerations that require thoughtful planning and execution.

Security in the age of AI

The security implications of AI operations reach beyond traditional cybersecurity concerns. AI agents require access to sensitive operational data to function effectively, creating new potential attack surfaces. With 91% of organizations prioritizing cybersecurity initiatives, security teams must be involved from the earliest planning stages.

The key is finding the balance between innovation and protection. Successful organizations implement "secure by design" principles for their AI operations, incorporating security guardrails that protect sensitive data while still allowing AI agents the access they need to function effectively. This isn't about locking everything down but creating appropriate boundaries that enable safe innovation.

Risk management strategies

Mitigating risks around AI deployment requires a multi-faceted approach:

1. Start small with well-understood use cases where the potential for unintended consequences is limited.
2. Implement comprehensive monitoring to track AI agent actions and decisions.
3. Maintain human oversight, particularly for critical systems or customer-facing operations.
4. Create clear escalation paths when AI agents encounter situations outside their parameters.
5. Regularly audit AI agent performance and impact against expected outcomes.

These strategies help organizations move forward confidently while maintaining appropriate guardrails around their AI operations initiatives.

Compliance considerations

The regulatory landscape for AI continues to evolve rapidly. Organizations must navigate requirements around data usage, privacy, transparency, and decision-making accountability. This is especially critical in regulated industries like healthcare, financial services, and telecommunications.

An effective compliance approach for AI operations includes:

• Maintaining comprehensive documentation of AI agent capabilities and limitations
• Ensuring traceability of AI agent actions and decisions
• Creating mechanisms for explaining AI agent recommendations when required
• Regularly reviewing AI operations against evolving regulatory requirements
• Engaging proactively with regulatory bodies when introducing significant new AI capabilities

Change management strategies

The human side of transformation remains as critical as the technical implementation. Successful AI operations initiatives directly address employee concerns through:

• Clear communication about how AI will augment rather than replace human capabilities
• Training programs that help team members understand and collaborate with AI agents
• Celebration of early wins that showcase the value of human-AI collaboration
• Recognition and rewards for teams that effectively incorporate AI agents into their workflows
• Continuous feedback loops that ensure human perspectives shape the evolution of AI operations

Implementation framework

Organizations seeing the greatest success with AI operations follow a structured approach to implementation:

1. Assessment : Evaluate your current operations maturity and identify specific pain points that AI could address.
2. Prioritization : Select initial use cases based on business impact, technical feasibility, and organizational readiness.
3. Pilot : Implement AI agents in a controlled environment with clear success metrics.
4. Validation : Measure outcomes against baseline performance and refine approaches based on results.
5. Scaling : Expand successful implementations across additional teams and use cases.
6. Governance : Establish ongoing oversight to ensure AI operations continue to deliver expected value.

This framework enables organizations to move methodically from concept to implementation, managing risk while capturing the substantial benefits that AI operations can deliver.

When AI operations are implemented thoughtfully, with attention to both technological and human factors, they become a cornerstone of operational resilience and competitive advantage.

The PagerDuty AI operations advantage

PagerDuty's decade-plus of AI innovation and deep operational data expertise uniquely positions it to help organizations successfully implement AI agents that deliver measurable business value.

• Deep Data & Domain Expertise. Built on 15 years of operational data from billions of interactions, PagerDuty AI delivers unmatched depth and accuracy in operational intelligence, surpassing competitors' generic AI models lacking specialized knowledge.
  
• Enterprise-Grade Guardrails. Comprehensive governance controls minimize AI hallucinations and harmful content. This enables customers to derisk transformation efforts and confidently deploy AI while maintaining compliance and operational integrity.
  
• Immediate Time-to-Value. Works out of the box, requiring no setup or new infrastructure, using the best models for each use case, avoiding single-model limitations. Embedded AI across the unified platform help teams start using AI immediately.
  
• Unified AI Ecosystem. Cross-agent interoperability powered by 750+ integrations across the platform. Agents collaborate seamlessly and with shared context through secure protocols.

Join the operations leaders embracing AI agents with a trusted partner who understands the technology and the human elements of operations transformation. By combining deep operational expertise with purpose-built AI technology, PagerDuty offers more than just tools-it provides a proven path to operational excellence in the age of AI. Explore PagerDuty AI agents.