Even the best site reliability engineers (SREs) spend too much time doing reactive work—triaging incidents, gathering context, escalating to the right teams, and documenting what happened. That work is essential, but it’s not where an SRE’s highest value lies. 

These engineers are hired to build and maintain resilient systems, not play air-traffic control with every alert that hits their queue. But as modern architectures become more complex, even the most capable teams end up stuck in a reactive loop. They spend so much time responding to repeat incidents that they have little capacity to address the root causes, and that simultaneously increases burnout and slows innovation.

Agents let teams break that cycle. SREs can leverage agents to handle the repetitive tasks and work with them to address more complex situations. With this human + agent approach, SREs can focus on what truly drives performance for their team and the business: solving incidents at the root cause and moving past toil to focus on innovation. 

The rise of agentic operations

AI agents are already reshaping how work gets done across the enterprise. According to the latest PagerDuty survey, 75% of global organizations have already deployed AI agents, and 25% have launched five or more. What began as experimentation is now starting to define how teams operate.

This growth signals a broader shift in mindset. For years, organizations have accepted that even their most skilled engineers would spend part of their time on repetitive, low-value tasks. With AI agents, that’s no longer a given. 

This shift is especially significant for SREs. Every alert, correlation, and escalation they manage represents an opportunity for an agent to help. Instead of manually sorting through telemetry and incident data, agents can process signals in real time and surface the most relevant insights and recommended actions. 

As adoption grows, this technology will fundamentally rebalance how engineers focus their time and effort across tasks. SREs will move from being the first responders of digital operations to the architects who evolve it.

From firefighting to system design

Complementing the SRE role with AI agents is meant to improve their work, not completely take it over. Agents handle the correlation and context gathering that eats into response time. And, beyond gathering data, agents can also take action on behalf of SREs , including running diagnostics, summarizing and communicating findings, and even running approved remediations. 

This means SREs no longer have to run incidents end-to-end. Instead, agents handle the well-understood and toil-heavy work so SREs can redirect their energy and skills toward designing more resilient systems.

When SREs spend less time in reactive loops and more time on strategic work, the benefits extend well beyond just MTTR, with widespread organizational impact. Highlights include:

• Increased operational resilience: Using data and insights synthesized with agents, SREs can take those learnings and apply them back into their incident management processes and even deeper into the SDLC (software development lifecycle).

• Lower monetary and reputational costs: Automated resolution for well-understood issues means less customer impact. This translates to better customer experiences and less cost to the business from lost revenue and/or SLA penalties.

• Improved talent retention: By removing the grind of repetitive, unfulfilling work, SREs are more likely to stay longer in their roles. And, this extends to other teams, like engineers who are also pulled into firefighting.

In short, agents elevate both people and performance, helping teams build systems that are not only more reliable but also more rewarding to operate.

A partnership model for modern operations

Trust in AI agents is growing exponentially. Our international survey shows that 81% of executives trust AI agents to take action on behalf of their organization during a crisis, such as an outage or security event. But that trust depends on a model where humans and AI work together. 

For SREs, that means assigning the right kind of intelligence to the right type of work. At PagerDuty, we think about this as a three-tiered model:

Tier 1: Well-understood issues (agent-led): These are recurring incidents with known fixes, so they’re handled autonomously. Agents detect, diagnose, and remediate without human intervention, then they generate reports for review. Example: A known error signal prompts the agent to restart a system and document the resolution automatically.

Tier 2: Partially understood issues (collaborative): Agents analyze patterns, surface probable causes, and recommend solutions. Humans validate and approve actions. Example: When an API latency spike occurs across multiple microservices, the agent correlates logs and suggests the most likely dependency issue for the SRE to verify before remediation. 

Tier 3: Novel or complex issues (human-led): Engineers lead investigation and strategy while agents collect supporting context, manage communication, and handle relevant tasks. Example: During a cascading failure across several systems, the agent compiles incident history, gathers telemetry, and summarizes updates so engineers can focus on root-cause analysis.

This tiered approach empowers teams to scale both efficiency and expertise. Routine incidents resolve themselves. Complex problems get SREs’ focused attention, while agents handle the grunt work. 

For SREs, this means moving from constantly reacting to building systems that can learn and adapt with every incident. For business leaders, it translates into greater operational resilience, faster innovation, and more consistent and reliable customer experiences.

How PagerDuty helps SREs evolve

PagerDuty’s AI agents embed intelligence and automation across every phase of the incident management lifecycle. Backed by 16 years of operational expertise and billions of real incidents, PagerDuty is designed to make life easier for people managing modern systems.

Here are the agents we’ve built to help SREs and other teams spend less time fighting fires:

• SRE Agent intelligently diagnoses service disruptions, automatically surfaces key context from past incidents, recommends remediation steps, and executes approved actions.
• Shift Agent delivers intelligent on-call conflict resolution directly from Slack. It shares on-call schedules and upcoming shifts with users, detects PTO conflicts (Google Cal extension available), recommends available teammates for coverage, and facilitates the override via direct message. 
• Insights Agent provides on-demand conversational insights as well as proactive recommendations and actions to improve operations.
• Scribe Agent automatically delivers Zoom/MS Teams transcriptions to incident channels and combines them with chat history to generate structured summaries, draft status updates, and enrich post-incident reviews.

When SREs get their own agents, they gain the space to do the work they really care about. The outcome is an organization that runs smoother, learns faster, and gives its people the bandwidth to innovate. PagerDuty’s human + agent approach makes this possible. By embedding AI into every phase of incident management, we’re helping enterprises evolve from managing alerts to orchestrating intelligent operations.

Ready to give your SREs the time and space to focus on mission-critical work? Learn more about the PagerDuty agents.