Earlier this year at our H1 2026 launch, we announced PagerDuty’s vision for autonomous operations: a future where AI agents learn from every incident, prevent failures before they happen, and progressively automate so teams can focus on innovation instead of firefighting. Central to that vision is the continuous learning flywheel: a systematic approach where every incident becomes organizational intelligence that feeds back into your operations, making your systems smarter and more resilient over time.

For years, we’ve been thought leaders in blameless postmortems and Failure Fridays through our Postmortems feature and our Jeli acquisition. We’ve learned a lot from customers, and we’re evolving the post-incident experience to help you break the cycle of repeated incidents, save time, and build more resilient operations. Now, we’re taking the next step on that journey with Post-Incident Reviews in PagerDuty UI, now rolling out in Early Access.

Are Your Incident Learnings Getting Lost in the Chaos?

You know that learning from incidents is the only way to improve resilience over time. But we often hear from customers that their teams struggle with completion and follow-through. Post-incident reviews (PIR) get skipped, action items don’t get done, and the same incidents keep happening. Your team burns out and you’re stuck in reactive mode.

• Context-switching loses details. Teams jump between monitoring tools, Slack, ticketing systems, and documentation platforms. By the time someone writes the PIR, critical context has evaporated.
• Manual work takes too long. Writing comprehensive reviews from scratch takes hours that exhausted responders don’t have.
• Insights stay siloed. Even completed PIRs rarely feed back into systems that could prevent future incidents.

The result? Repeated incidents and missed opportunities to build more resilient operations.

The new Post-Incident Reviews, directly available in the PagerDuty UI, solve this by bringing the entire PIR workflow directly into the incident experience. Here’s a preview of what’s available today for Early Access customers.

Capture Learnings Without Leaving PagerDuty

Post-Incident Reviews are built directly into the incident experience in the PagerDuty UI. When your team resolves an incident, whether in Slack or the web interface, a post-incident review is automatically available with full incident context already accessible.

What you get:

• Start PIRs from where you work. Kick off a review directly from Slack when you resolve an incident, or from the incident detail page in the web UI. No separate tools or logins required. PIRs are automatically created when an incident is resolved, so your team never has to remember to start one manually.

• All context in one place. Every PIR includes the complete incident timeline, responder actions, service information, and alert data. PagerDuty automatically ingests context from Slack conversations, Scribe Agent summaries, and Incident Lifecycle Events (ILE), so everything documented in those locations is already there in the PIR. This means that to generate an initial PIR, there’s no need to hunt through Slack channels, dig through logs, or try to remember who did what. The incident history and context are right there when you open the PIR.

• Real-time collaboration. Multiple team members can work on the same review simultaneously with live cursors showing who’s editing what. No more version conflicts or lost edits, as everyone can see changes as they happen. Whether you’re the incident commander adding the timeline, an engineer documenting the root cause, or a manager reviewing the impact, everyone can contribute at the same time without stepping on each other’s work.

• Structured templates. Guide your team through consistent, thorough analysis with customizable templates that ensure nothing gets missed. Create templates with sections like “What Happened,” “Root Cause,” “Impact,” and “Lessons Learned” to standardize your PIR process across teams. You can customize these to match your organization’s specific needs and ensure every review captures the information that matters most to you.

Instead of treating post-incident reviews as an afterthought, they become a natural part of closing out every incident.

We’re establishing the baseline for the continuous learning flywheel. When teams can capture learnings immediately without friction, those insights become the fuel that powers smarter operations.

Let AI Do the Heavy Lifting

Coming soon in the next phase of the EA program, we’ll supercharge your PIRs with AI-generated content that turns hours of manual work into minutes of refinement.

What’s coming in our future plans:

• Instant AI-generated drafts. AI will automatically generate PIR content (summaries, key timeline moments, root causes, and suggested follow-ups) based on full incident context.
• Import Slack conversations with one click. You’ll be able to selectively import all messages or just pinned messages directly into your PIR narrative.
• Add attachments for deeper analysis. You’ll be able to upload runbooks, screenshots, logs, or supporting documentation to enrich your PIRs.
• Collaborate with comments and @mentions. You’ll be able to tag stakeholders, ask questions, and refine the narrative together. Keep cross-functional teams aligned on what happened and what needs to happen next.
• Create actionable follow-ups with AI assistance and Jira sync. Follow-up actions are available today in PagerDuty. Soon, you’ll get AI-suggested follow-up actions and the ability to sync them to Jira to track remediation work alongside your existing development workflows.
• Customize AI prompts. You’ll be able to create account-specific templates with custom AI prompts tailored to your organization’s PIR format. Mark sections as required and control AI generation on a section-by-section basis.
• Feed insights back into SRE Agent memory. PIR learnings will automatically feed back into PagerDuty SRE Agent’s memory, improving future incident response and helping developers assess deployment risk to prevent incidents before they occur.

The result is a continuous learning flywheel in action. Comprehensive learnings get captured in minutes instead of hours, feeding intelligence back into your operations to build more resilient operations.

Built for the Way You Work

Post-Incident Reviews in PagerDuty UI integrate seamlessly into your existing workflows:

• Slack-native experience. Start PIRs in Slack with the click of a button, import channel data, and collaborate.
• Web UI for deep analysis. Access the full PIR experience in the PagerDuty web interface with rich editing, timeline visualization, and valuable incident context.
• Jira integration (coming soon). Sync follow-up actions to Jira to track remediation work alongside your existing development workflows.
• API access (coming soon). Programmatically access PIR data to build custom integrations, analytics dashboards, or feed insights into other systems.

Ready to Get Started?

Post-Incident Reviews in PagerDuty UI is rolling out now in Early Access for customers on Professional plans and above.

Sign up for Early Access to start turning your incidents into prevention.

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Safe Harbor

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OPINION The future of AI is unwritten, but the writing is on the wall – your margin is my opportunity. Amazon founder Jeff Bezos said as much more than a decade ago in support of the e-souk's low-price, low-margin sales strategy. That opportunity exists in the AI training and inference business. But perhaps not for long. Two leading American AI companies, Anthropic and OpenAI, are not actually profitable at this point, but their pitch to investors is something along the lines of "just hang in there a few more years and keep sending cash." Given reports that Claude Code subscribers paying $200 a month can potentially consume $5,000 worth of tokens and that OpenAI is also losing money on subscriptions, it starts to become a bit clear why Anthropic, OpenAI, Google, and Microsoft have already started pushing customers toward metered usage pricing. AI revenue needs to go up for frontier model makers to survive. And then AI adoption needs to grow. Government agencies and large corporations that don't keep a close eye on fees may be terrified enough of AI-enabled exploitation to pay a premium for models like Anthropic's Mythos and OpenAI's GPT-5.5. But more price-sensitive folk may shop for cheaper tokens. And they're likely to find them. Benedict Evans, among the more astute industry observers, expects AI models will be commoditized. In his recently updated presentation, "AI eats the world," he suggests that the AI supply/demand imbalance will ease and the pricing power of leading AI labs will dissipate. He argues that models will become commodity infrastructure and that innovation and pricing power will have to move up the stack. That's already evident in Anthropic's efforts to keep developers interacting through its own tools like the Claude Code CLI and desktop app, and through services that sit atop its models like Claude Cowork, Claude Design, and Claude for Creative Work. But it's more apparent in US companies lobbying for regulatory intervention as a defense against competition from China, some of which has taken the form of copying AI models via a process called distillation. Zilan Qian, a research associate at the Oxford China Policy Lab, recently explored how software developers in China are acquiring AI tokens for pennies on the dollar. She writes that despite the fact that leading US model makers try to prevent people in China from using US models, everyone who wants access can get it through API proxies. "The logs they generate may have become a commodity, traded for purposes ranging from model training to targeted fraud," Qian wrote. "Meanwhile, every layer of control frontier US AI companies have added (geoblocking, phone verification, credit card requirements, and now live biometric KYC checks) has produced a corresponding layer of evasion infrastructure." This process may not be savory or sustainable – Qian posits these token sellers are just trying to acquire customers and obtain data – but it points to the difficulty US firms will have maintaining their margins and their exclusivity. Open weight models like GLM-5.1, Kimi K2.6, DeepSeek V4-Pro, and Qwen3-Coder-Next are already adequate for less demanding software development work and some, like Qwen3.6-27B, run quite well on suitably provisioned local hardware. US companies are estimated to have a lead of about seven months on Chinese AI companies. But that race will not go on forever. Even if US AI models continue to improve at their current pace, open weight models from China and elsewhere should match current leaders Claude Opus 4.7 and OpenAI GPT-5.5 by the end of 2026. At that point, better benchmarks will no doubt be welcomed, but they won't be necessary. Commodity AI will be good enough for enterprise and entrepreneurial software development. And maybe other uses will emerge, but coding right now is what people are paying for. As noted by Andreessen Horowitz, annualized AI spending by enterprises reached $3 billion annually for coding. In other categories (legal $500 million, support $400M, and medical/health $300M), adoption is significantly less. Looking at Evans's "AI eats the world" figures, promoting AI adoption will be a challenge. The tech industry is the only US workplace sector where more than 25 percent use AI on a daily basis. In finance, professional services, healthcare, retail, manufacturing, and government, there's less daily usage. And in the consumer space, only five percent of ChatGPT’s 900 million-plus weekly users pay for the privilege. Among software developers, most of those using AI are not trying to apply it to cutting-edge research or to develop complex attack chains. They're using it for fairly well understood software applications and workflows, or they're experimenting with AI agents. And increasingly, it looks like they can buy tokens at a discount if that matters. Anthropic and OpenAI need pricing and adoption to go up in order to thrive. Their margin is their vulnerability. They're going to strike deals with incumbents to make their models available on desktop and mobile hardware, particularly given the space and power constraints of phones. That will come at a cost. The likely winners will be the companies that control software distribution and delivery – operating system vendors like Apple, Google, and Microsoft, and cloud service providers like Amazon, Google, and Microsoft. Absent regulatory or legal barriers, supply constraints, or practical obstacles, prices face downward pressure where margins are high. And when you're many billions in the hole like Anthropic and OpenAI, that makes escape more difficult. In his presentation, Evans observes, "Sometimes software eats the world, and sometimes it only nibbles." ®

Ronda Rousey and Gina Carano fought for 17 seconds longer than they should have over the weekend.

Their brief Saturday encounter in a Southern California hexagon, a comeback for both after ridiculously long layoffs yet still promoted by Jake Paul’s MVP outfit as the biggest women’s MMA fight in history, ended as soon as Rousey set Carano up for an arm bar, the ex-judoka’s trademark finishing move from back when she was relevant. Carano, who gave up the cage for acting and right-wing mouthpiecing, tapped quicker than Fred Astaire. 

https://www.youtube.com/watch?v=Rg4OWWAEoCY

“I have travelled all over the world in the last 30 years, and have never seen anything like the density of assholes I just encountered in Japan, [i.e.] tourists being an unbearable menace specifically while on and around their phones.”

Here’s a famous optical illusion, which was developed by the American neuroscientist Edward H. Adelson.

Even though square A appears darker than square B, the two are, in fact, the exact same shade of gray. It’s such a powerful illusion that, even knowing the illusion doesn’t destroy its effect; you’ll still “see” the illusion after you know about it. It’s so powerful that you may not believe me over your lying eyes. If you’re on macOS, you can confirm the illusion by opening the Digital Color Meter app and hovering your mouse pointer over each square in turn. You’ll see that both squares have the same RGB value. In hex, the value is #646464.

I’m going to suggest two stylized reactions to witnessing this illusion. One reaction is to say, “Oh, no! This illusion clearly illustrates a flaw in the human visual system! We should work on developing a vision correction technology so that people don’t fall victim to problems that would arise from this failure mode in human visual processing.”

A very different reaction is to say, “Oh, wow! This illusion gives us a hint into how the human visual system functions! Our brain must contain a prior model about the relationship between light, shadow, and objects, and is imposing that model when processing the signals coming from our optic nerve. This illusion appears to be an example of a pathological case which violates the human brain’s model.”

The first reaction is, admittedly, a ridiculous strawman. These sorts of illusions are harmless, so there’s no motivation to try to “correct” from them. After all, it’s no coincidence that the illusion was developed by a researcher who studies human vision. Even though our visual system is failing us in this strange case, the value of an illusion like this is not to learn the circumstances in which our vision fails, but instead to use the failure to gain insight into how our vision works so effectively for the vast majority of the time.

Last week, I wrote a post about Safety-II, the idea that we will learn more about how to create reliability in our system by studying the (common) successful cases rather than the (rare) failure cases. But we can also use the failure cases to learn about how the system normally succeeds! Just as neuroscientists can use optical illusions (where the vision system fails) to learn how the visual system succeeds, we can use incidents (when our system fails) to learn about how our system succeeds.

To make this more concrete, imagine you’re in an incident review meeting, and one of the incident responders, someone who is a real expert at your company, is talking about how, in hindsight, they misdiagnosed the problem during the incident. The signals that they saw misled them until thinking that the system was in state A, when really the system was in state B. And that led to the incident taking much longer to resolve, because the responders went down the wrong path.

The typical sort of question to ask in a review meeting would be along the lines of “what can we do to make sure we don’t misdiagnose this type of problem in the future?” But, there’s a very different question that you ask. And that question is, “how did the responder come to the conclusion the system was in state A?” Asking this question will expose details about the responder’s mental model of how the system actually works. If the responder was an expert, and they were led astray by the signals, then it’s likely that this incident was a pathological case, an operational equivalent of the optical illusion we saw above. By asking the responder about how they made the diagnosis, you are giving the meeting attendees the opportunity to learn from the expert responder. Similarly, you can ask the responder, “how did you finally figure out that the system was in state B?”, which will give you another chance to retroactively witness the work of an expert in action.

Like optical illusions, incidents are pathological cases. But, unlike illusion, incidents aren’t harmless. This means that the natural reaction is, “what went wrong here, and how do we stop doing that?” But if our goal is improvement, we should recognize there’s a lot more leverage in maximizing the opportunity to learn about what’s working well today, from the experts who are doing that work well. After all, there’s a reason we called that responder an expert; their work had led to a lot more success than failure.

With David Cronenberg recognized as the subgenre’s preeminent figure, Katie Rife digs deeper into body horror with a starter pack of twenty films by other directors going to gooey, goopy extremes.