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PagerDuty Announces Spring 2026 Release

PagerDuty announced the next upcoming generation of the PagerDuty Operations Cloud, transforming how enterprises achieve digital reliability and advance down the path towards autonomous operations. 

By transitioning from reactive response to autonomous operations, PagerDuty will enable a future where reliability is built on a foundation of resilience and proactive prevention.

PagerDuty is reinforcing its foundation with its plans to bring full lifecycle incident management directly into the environments where developers live.

  • Slack-Native Agentic Workflows: A completely reimagined class-leading ChatOps experience allows teams to run the entire incident lifecycle without leaving Slack.
  • Human-Centric Mobilization: Enhanced schedules and ChatOps capabilities ensure the right experts are mobilized immediately, integrated seamlessly with PagerDuty’s enhanced post-incident reviews to help ensure every disruption ends with documented institutional knowledge.

PagerDuty is evolving its SRE Agent into a virtual responder. The PagerDuty SRE Agent will deeply integrate into the team’s roster and escalation policies. The new and enhanced features and capabilities will include:

  • Autonomous Detection, Triage, and Diagnosis: The SRE Agent can be the first on the scene. It can identify anomalies via AIOps, assess the tech stack, and perform deep diagnostics before a human is ever awakened.
  • Workflow Integration: By leveraging the Model Context Protocol (MCP) and an expanded API library, the agent can connect to a customer’s entire stack—including observability tools, internal developer platforms and developer environments. PagerDuty allows teams to work in the environments they prefer (web, mobile, or chat ops), and integrate that data into existing workflows.
  • Enhanced Integration Support: PagerDuty supports streamlined authentication to popular software development tools. Teams can connect once and leverage these integrations across API workflows and the SRE Agent, with granular permissions to control data access.
  • Agents Built on PagerDuty Foundational Model: PagerDuty leverages 16 years of historical data to build and refine its models. This built-in expertise creates a context flywheel that continuously improves by capturing how teams respond to incidents and applying those learnings to future events.

PagerDuty offers a context flywheel, a systematic approach to continuous learning that compounds value over time as data is captured and learning is applied.

  • Capturing Key Moments: While other agents only see machine data, PagerDuty captures the key moments—the hypotheses, chat records, and decisions made by human responders during a crisis.
  • Continuous Learning: This internal and external data flows into the PagerDuty platform. The output is a virtuous cycle: smarter automated responses, more accurate root cause analysis, and the ability to push context back to developers to fix issues at the source. The PagerDuty platform allows your agents and humans to work together to detect patterns, solve incidents, and apply learning to preventing incidents in the future.
  • Prevention at Scale: By pushing incident data back to developers via MCP, IDPs and other tools, PagerDuty helps coding agents and engineers understand past failure patterns, allowing them to remediate root causes in the codebase and prevent incidents from recurring.

PagerDuty also announced upcoming expanded agent-to-agent functionality. Through enhanced advanced MCP functionality, PagerDuty’s SRE Agent will be able to interact with other AI ecosystem agents, such as AWS DevOps Agent and Azure AI SRE. This creates a collaborative, multi-agent fabric that ensures PagerDuty remains the central nervous system for the autonomous enterprise.

“Reliability is the result of resilience plus prevention,” said David Williams, senior vice president of Product at PagerDuty. “With the upcoming launch of the PagerDuty SRE Agent as a virtual responder, we are providing the connective tissue between AI-driven infrastructure and human expertise. We will be the only platform that can capture the key moments of an incident—the tribal knowledge and human decisions—and turn them into a continuous learning system that prevents future disruptions before they impact the business.” 

SRE Agent as a Virtual Responder: Early access available in Q2 2026.

SRE Agent as a Fully Autonomous Responder: Early access available in H2 2026.

Agent-to-agent MCP capabilities for, including SRE Agent, Scribe Agent, and Shift Agent, will reach general availability in H1 2026. 

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PagerDuty Announces Spring 2026 Release

PagerDuty announced the next upcoming generation of the PagerDuty Operations Cloud, transforming how enterprises achieve digital reliability and advance down the path towards autonomous operations. 

By transitioning from reactive response to autonomous operations, PagerDuty will enable a future where reliability is built on a foundation of resilience and proactive prevention.

PagerDuty is reinforcing its foundation with its plans to bring full lifecycle incident management directly into the environments where developers live.

  • Slack-Native Agentic Workflows: A completely reimagined class-leading ChatOps experience allows teams to run the entire incident lifecycle without leaving Slack.
  • Human-Centric Mobilization: Enhanced schedules and ChatOps capabilities ensure the right experts are mobilized immediately, integrated seamlessly with PagerDuty’s enhanced post-incident reviews to help ensure every disruption ends with documented institutional knowledge.

PagerDuty is evolving its SRE Agent into a virtual responder. The PagerDuty SRE Agent will deeply integrate into the team’s roster and escalation policies. The new and enhanced features and capabilities will include:

  • Autonomous Detection, Triage, and Diagnosis: The SRE Agent can be the first on the scene. It can identify anomalies via AIOps, assess the tech stack, and perform deep diagnostics before a human is ever awakened.
  • Workflow Integration: By leveraging the Model Context Protocol (MCP) and an expanded API library, the agent can connect to a customer’s entire stack—including observability tools, internal developer platforms and developer environments. PagerDuty allows teams to work in the environments they prefer (web, mobile, or chat ops), and integrate that data into existing workflows.
  • Enhanced Integration Support: PagerDuty supports streamlined authentication to popular software development tools. Teams can connect once and leverage these integrations across API workflows and the SRE Agent, with granular permissions to control data access.
  • Agents Built on PagerDuty Foundational Model: PagerDuty leverages 16 years of historical data to build and refine its models. This built-in expertise creates a context flywheel that continuously improves by capturing how teams respond to incidents and applying those learnings to future events.

PagerDuty offers a context flywheel, a systematic approach to continuous learning that compounds value over time as data is captured and learning is applied.

  • Capturing Key Moments: While other agents only see machine data, PagerDuty captures the key moments—the hypotheses, chat records, and decisions made by human responders during a crisis.
  • Continuous Learning: This internal and external data flows into the PagerDuty platform. The output is a virtuous cycle: smarter automated responses, more accurate root cause analysis, and the ability to push context back to developers to fix issues at the source. The PagerDuty platform allows your agents and humans to work together to detect patterns, solve incidents, and apply learning to preventing incidents in the future.
  • Prevention at Scale: By pushing incident data back to developers via MCP, IDPs and other tools, PagerDuty helps coding agents and engineers understand past failure patterns, allowing them to remediate root causes in the codebase and prevent incidents from recurring.

PagerDuty also announced upcoming expanded agent-to-agent functionality. Through enhanced advanced MCP functionality, PagerDuty’s SRE Agent will be able to interact with other AI ecosystem agents, such as AWS DevOps Agent and Azure AI SRE. This creates a collaborative, multi-agent fabric that ensures PagerDuty remains the central nervous system for the autonomous enterprise.

“Reliability is the result of resilience plus prevention,” said David Williams, senior vice president of Product at PagerDuty. “With the upcoming launch of the PagerDuty SRE Agent as a virtual responder, we are providing the connective tissue between AI-driven infrastructure and human expertise. We will be the only platform that can capture the key moments of an incident—the tribal knowledge and human decisions—and turn them into a continuous learning system that prevents future disruptions before they impact the business.” 

SRE Agent as a Virtual Responder: Early access available in Q2 2026.

SRE Agent as a Fully Autonomous Responder: Early access available in H2 2026.

Agent-to-agent MCP capabilities for, including SRE Agent, Scribe Agent, and Shift Agent, will reach general availability in H1 2026. 

The Latest

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...