Product Launch: AI and Automation are Rewriting the Operations Playbook

Davis Godbout: I'm Davis Godbout, Senior Director of Product Management here at PagerDuty. Today, I'll be sharing how PagerDuty is helping our customers rewrite the operational playbook in the era of AI and automation, giving more time and power back to the developers.

Joining me are product managers from across PagerDuty. Mya?

Mya King: Hey. I'm Mya, and I'm a Senior Product Manager for the Mobilization team. And I'm excited to share flexible schedules and the Shift Agent with you today.

Julia Nasser: Yep. And I'm Julia. I'm really excited today to share more about the SRE agent with you.

Davis Godbout: And Sean.

Sean Noble: And I'm Sean Noble. I'm a Product Manager on our AI team, and I'll be sharing how our integrated developer experience, spanning both MCP and Backstage connects AI tools back to PagerDuty.

Davis Godbout: Alright. Before we get started, this presentation shares forward looking statements. In discussing our future plans, we acknowledge uncertainties. Forward looking statements are subject to risk. Of course, actual results may vary.

So in a recent survey conducted by PagerDuty of over fifteen hundred IT and business executives, more than four out of five companies are using AI to write, review, or suggest code. This innovation boom is now the norm. It's not just tech forward companies. It's the new mode of doing business.

More code is being written than ever before.

Based on the sheer volume of change alone driving major innovation, organizations are also feeling the burden of more risk and reliable platforms.

Yeah. And we've all seen this. Yeah. Saw the news last week. You know, outages happened.

Failure will happen. Software will break. We see it every day. But as normal as failure is, every time an incident happens, it's disrupting your business and your people.

The cost varies, ranging from customer experience impacts to brand reputation to revenue and more.

Meanwhile, as organizations are unlocking innovation, teams are losing time to these disruptions, and this is the real cost of the business. You can't get that time back for work that matters. Teams are stuck in this cycle of break fix with no way out. This is where the goal is to meet fire with fire, complementing AI driven innovation with AI driven operations. A higher volume of code ship requires more AI powered automated operations in a continuous learning cycle to evolve your organization.

This is how you move forward with AI with confidence, delivering more value at scale to customers and spending less of your team's resources on break fix cycles.

We know this because we've seen it all helping over thirty thousand companies manage their incidents every day. We empower developers to spend less time fighting fires and more time building. Our offering is reliable, enterprise grade, and flexible. We're dedicating significant development resource ensuring that our customers continue to get the best incident management experience that any partner could provide. One with AI and automation built into the experience.

While many vendors are still experimenting with AI concepts, PagerDuty is leading the charge with production-ready, AI capabilities that apply to real operational challenges. We've been clear that scattered signals lend AI assistance to complex workflows and with autonomous agentic operations drive real opportunities for change, synthesizing data faster than any human is capable of and driving continuous improvement.

In PagerDuty, every incident is a reinforced system that not only enforces consistent outcomes, but drives continuous improvement as we move towards a more autonomous future. Everything you do in PagerDuty is captured as data and distilled into learnings as post-incident reviews. That data then feeds back into how you run instance the next time because they're gonna happen. In tandem, the platform's AI-powered capabilities take your unique incident management data and feed those into customized insights, making the system more resilient over time.

Eventually, many instances could be vented entirely or resolved with AI agents that work without human intervention beyond supervision. But we wanna help you get there. That's the key.

So taking a step back, this is where our integration ecosystem really stands out. We snap into your overall operational data ecosystem with all key tools from customer service, monitoring, logs, developer tools, systems record, and more. And it's continually evolving. PagerDuty acts as a system of intelligent action to empower your team at all the right moments. With PagerDuty, you can work through the entire incident lifecycle the way you choose, whether that's detecting your mobile, triaging in chat, diagnosing in the UI, or resolving your IDE. We're not rigid. We're not opinionated, and you can access your PagerDuty data from anywhere and execute actions from where you prefer.

We have over seven hundred and twenty five integrations and growing. We ensure we can deliver operational resilience, and this experience is always improving.

So you need this to work in production. This is how we're investing in our platform to make this vision real. With this launch, we're extending our end-to-end incident management leadership, introducing flexible schedules in our Shift Agent, adding long sought after features like re-notify responders, reopening incidents, and more.

And continuing our efforts to improve platform reliability with introduction of SCIM plus enhancements to our ServiceNow Jira integrations.

We're also strengthening our capabilities for AI-powered operations and enhancements to the SRE Agent, the brand new Scribe Agent, and remote local MCP support for a more connected AI ecosystem.

Last but not least, we’re providing an integrated developer experience so users can work wherever it suits them, whether that's using MCP enabled clients like an IDE or Copilot, chat apps, or Backstage. Let's see what this all looks like in action.

Narrator: Online retailer Greenagonia is launching a highly anticipated new product. The company has doubled down on developer productivity and operational resilience to deliver a seamless customer experience that drives revenue. They've chosen PagerDuty to transform incident management. The power of PagerDuty is in building intelligence to help you respond to incidents faster and more efficiently.

Every incident teaches the system something new. Problems that once needed entire teams now get handled by fewer people with the help of AI. Eventually, they're prevented entirely. It's a continuous learning loop to help teams automate more so they can respond faster with fewer people and focus on innovation. Let's see it in action.

Ahead of such a critical launch, operations manager Maya is ensuring multiple people are on call at all times.

Alex, one of the responders in the schedule, books time off. The Shift Agent promptly detects the conflict, finds teammates who can cover the shift, and facilitates the override. That was easy. It's launch day. Online orders start to flood in from Europe. Response times spike in the EU region, triggering an incident. Using model context protocol or MCP, PagerDuty's SRE Agent pulls real-time metrics from Grafana.

Web server requests are timing out. The SRE Agent is recommending I scale up web server instances, so let's try that.

It worked. The SRE Agent generates a runbook that will automatically provision more instances the next time something similar happens.

Another issue comes up related to the gateway timeouts, this time in the US.

Processing failures are spiking. It's reassigned to the right service team and escalated to a major incident. The major incident workflow automatically creates a Zoom meeting and adds the Scribe Agent. The agent transcribes Slack and Zoom conversations to help responders get up to speed. It also drafts stakeholder updates and captures key moments for post-incident reviews so responders can stay focused on resolution.

The team needs more database expertise and calls back one of the initial responders.

Wade checks and clears the database connections pools, which looks like it works, but the problem comes back. The responder reopens the incident with all history intact.

Looks like we're hitting payment gateway rate limits.

The SRE Agent automatically pulls gateway logs from Datadog and correlates recent deploys from GitHub. It even proactively fetches and recommends a runbook for the situation.

The playbook says to run the payment queue failover workflow. Let's try that.

The incident is resolved. Before closing it out, Greenagonia tracks key data on all incidents for compliance and reporting. PagerDuty automatically creates a summary for the post-incident review so that Greenagonia can learn from what happened. Service owners can also see incident history and on-call coverage to work on improving reliability directly from the PagerDuty plug-in in Backstage.

The integration maps critical service objects, such as service tier, directly to PagerDuty's service custom fields.

Now I can see how response times differ by service tier, and Tier One services are continuously getting better.

Later, the Insights Agent delivers proactive recommendations.

AI orchestrations identified an event pattern behind the major incident and suggests automatically classifying future cases as P1 to kick off the appropriate workflows.

This is gonna save us so much time. What else can we automate?

With PagerDuty, Greenagonia turns every incident into an opportunity to improve reliability, freeing developers to focus on innovation.

The result - happy customers, a fast growing business, and a revenue generating product launch. Build more resilient operations with PagerDuty today.

Alright. Now I wanna bring on Mya. First up, let's talk about something that's been on everyone's minds for a while. You all have been super vocal about it: scheduling and on-call management.

Mya King: Thanks, Davis.

Today, our current layer-based scheduling system is creating problems across the board. Managers are bogged down in time consuming schedule creation. Rotations are too rigid to flex with real world needs, and scheduling conflicts remain invisible until coverage gaps reach a critical point. Making operational resilience the standard, means freeing your best responders from all the scheduling overhead.

With PagerDuty's new and modernized scheduling system, you can easily build iCalendar-based rotations using quick start patterns, making it simple to have multiple responders on call from a single schedule to support primary and shadow shifts.

With enhanced visibility, you'll identify coverage gaps and fill them within minutes.

With our calendar integration, PagerDuty automatically shows responders’ time off, identifies on-call conflicts, and sends alerts to the right people, eliminating those unexpected on-call surprises, thus eliminating the redundant scheduling work that burns out your best people and makes operational resilience standard across your organization.

But here's where it gets interesting.

Meeting industry standards was just the baseline. What sets PagerDuty's modernized scheduling system apart is the Shift Agent. Today, responders and schedule managers are drowning in manual work just to handle on call conflicts effectively.

The process is fragmented and exhausting. You're constantly toggling between calendars and on-call schedules, hunting down available replacements, reaching out individually to confirm availability, then manually updating schedules across multiple systems. It's not just time consuming, it's also error prone. With so many moving parts, coverage gaps slip through unnoticed until it's too late, putting your operations at risk. This is where intelligent override management becomes critical to preventing operational failures. The Shift Agent eliminates this burden by automatically ingesting data from both work calendars and PagerDuty schedules. It detects on-call conflicts in real time and streamlines the entire override process into just a few clicks.

What used to take a few hours of coordination now happens in moments, with greater accuracy and zero missed coverage.

And we didn't stop at schedules. We've been equally focused on enhancing our core incident management capabilities.

Over the last few months, we've shipped over one hundred and fifty improvements. Seriously, check out the release notes if you want proof. And we're just getting started. Let me highlight three enhancements that really stand out.

With required fields, enterprise customers can configure which fields are mandatory, ensuring compliance requirements are always met. Furthermore, required fields during resolution prevents incidents from being closed until critical data is captured. When resolved turns out to be premature, you can now reopen incidents seamlessly instead of creating new ones, one of our top feature requests.

Lastly, during an active incident, send fresh notifications to responders who need a nudge as situations evolve.

These were some of our most requested features, and we're thrilled to deliver them and help you strengthen your incident management workflows and make your day to day operations smoother.

That wraps up PagerDuty's end-to-end incident management preview. Feel free to drop any questions in the Q&A, and I'll respond at the end. Now I'm excited to bring Julia to share how we're evolving incident management to new AI-powered operations.

Julia Nasser: Thanks, Mya. So systems are complex, and incidents are taking longer to resolve. Teams are often swarming trying to find the information they need because knowledge is siloed, and they're solving incidents with inefficient processes. The PagerDuty SRE Agent is basically your AI teammate that accelerates triage, diagnosis, remediation, and learns from incidents to prevent reoccurring issues.

It works by gathering data and signals from across your entire tool stack, turning logs, metrics, and service topology into actionable insights. And here's the cool part. The SRE Agent has memory. With every response, it gets sharper.

You get stronger post incident reviews, smarter runbooks, and built-in learning that unlocks automated operations. We currently support integrations with Datadog, AWS CloudWatch, and Grafana for logs, as well as Confluence and GitHub for documentation, so your team stays focused on shipping the next great thing for your customers. And customers are already seeing double digit reductions in time to resolve by using SRE Agent.

And now let me go ahead and introduce you to another new agent, Scribe. As developers move and build at the speed of AI, new, more complex issues arise. During critical incidents, teams need to respond dynamically, but they're still stuck with static processes. For example, incident commanders waste precious time manually writing status updates from scattered communications, often missing key information. Also, team members that join mid-incident struggle to quickly understand what's happening. And finally, the manual burden of documenting every decision pulls teams away from what truly matters, resolving the incident.

Our Scribe Agent transforms this process by automatically transcribing Zoom, MS Teams meetings directly to the incident channel. It generates structured status updates and summarizes key decisions and clear action items. This eliminates the burden of manual documentation while ensuring stakeholders stay informed and nothing gets overlooked. It creates a comprehensive incident record that's invaluable for efficient response and meaningful post incident reviews.

And now let's talk about how PagerDuty is helping teams get more proactive results outside of incidents, even preventing future reoccurrences. So as AI accelerates development and your team ships more code than ever, spotting operational risks becomes even more challenging. Identifying patterns and extracting insights from growing volume of data takes a significant manual effort. Optimization opportunities are sometimes only uncovered during quarterly reviews or after major incidents.

This reactive approach creates slow decision making cycles that prevent teams from getting ahead of potential issues. The Insights Agent helps teams by proactively surfacing optimization opportunities before they become problems. Instead of hunting through dashboards, teams get context-aware answers to critical questions and actionable insights to help improve operations, all from within Slack. This empowers both technical and nontechnical teams to make faster, more informed decisions.

Our early access users are already saving up to thirty minutes per analysis, and they love being able to do it without even leaving the chat.

And our last feature that we're going to review is called AI Orchestrations. And this feature works by analyzing event data to surface improvement areas that can be really challenging to find manually. Sometimes even once those areas are identified, nontechnical or new users may find it time consuming or difficult to build their own automations. This results in slower adoption and outdated automations.

But with AI Orchestrations, customers can shift from reactive to proactive by leveraging AI to identify actions to automate based on past responder behavior. And the way it works is that AI Orchestrations uses machine learning that identifies historical event and incident data. And based on that analysis, it will recommend actionable rules to create new event-driven automations. With a couple clicks, users can apply recommended rules that reduce noise, improve triage, and ultimately lower time to resolve.

So teams spend more time innovating and less time on manual toil.

Okay. So now I'll go ahead and hand it off to our last guest today, Sean. If you have any questions for me, I'll stay on for Q&A, and you can go ahead and drop your questions in the chat.

So, Sean, you've been at the forefront of PagerDuty's AI strategy for how we're growing our ecosystem. MCP is obviously a critical part of that. Do you wanna share what you've been working on?

Sean Noble: Yeah. Absolutely, Julia. We’d love if all these innovations meant that humans never had to respond to another incident again, but that's not realistic as everyone here knows. So we wanna make sure that the incident management experience is one where our users feel comfortable. This is where our developer experience, including MCP, comes into play.

Everyone's looking at how AI can make tool ecosystems more interconnected.

Today, developers are stuck context switching between AI tools and operational data and build custom integrations for every AI platform.

Meanwhile, critical incident service and schedule data takes too long to access, blowing incident response when every second counts.

PagerDuty's MCP server fixes this by letting developers use natural language commands to access PagerDuty data via the standardized model context protocol. This means that a universal connection to incident management data from any MCP compatible surface like Claude Code, Cursor, or OpenAI.

It's a more efficient way to connect tooling, and customers are loving it. Hundreds have adopted in the first few months.

MCP isn't the only integration enhancement. We're also announcing a big update with our partner, Backstage.

Incident responders often waste time hunting for the context that they need to resolve issues quickly. At the same time, service owners, SREs, and platform engineers need to ensure that their services aren't drifting towards incidents. With the PagerDuty plug-in for Backstage or the commercial version, Portal, teams can view open incidents, create new ones, see who's on call, review escalation policies, and check recent changes all in a couple of clicks. This drives faster resolution with the most relevant context right in existing workflows.

No switching interfaces, just meeting developers where they work.

One more exciting partnership, PagerDuty is now part of the Glean MCP directory. This brings together two leaders in AI-powered productivity and operations, making it easier to connect PagerDuty's incident data to any AI tool or agent through the standardized MCP and Glean.

PagerDuty is the first and currently only incident management partner in Glean's AI ecosystem.

With our Glean integration, you could surface incident, service, and schedule information, take actions, and automate workflows wherever your teams work, whether that's in chat, in your favorite AI assistant, custom agent.

It's about meeting teams where they are, breaking down silos, and unlocking the full value of operational data. We're not stopping there. We're working with Amazon Q, Amazon Quick Suite, GitHub, Logz.io, and others to ensure that customers can connect to their AI ecosystems for faster, more developer-friendly incident management.

Now I think that's it for us. Davis, did you wanna switch over to Q&A?

Davis Godbout: Absolutely. And, thanks so much for the lively discussion, chat, and Q&A. So I'll start, kick them off, and, I'll start with Julia.

And it's a great question around how far have you been able to get with AI for Microsoft Teams?

Now our understanding is that enabling features in Teams is not as easy as Slack. Have we run into this at all, Julia?

Julia Nasser: Yeah. Absolutely. So just, kind of, that's a great question. So we do have a kind of what I'd call our PagerDuty advanced product.

So our base PagerDuty advanced assistant is available now in MS Teams or Microsoft Teams. Also, we do have our Scribe Agent, which can transcribe both Zoom and Microsoft Teams as well. Microsoft Teams is currently in early access for our Scribe Agent. For Insights, Shift, and SRE Agent, we are planning that for next year.

So that is actively on our road map.

Great question.

Lots coming soon, available now, and coming soon for Microsoft Teams.

Davis Godbout: Exactly. So that lines up with the next question there was around, you know, a lot of questions around Microsoft Teams. And, you know, the purpose here is, you know, we're starting with a model of learning in Slack and then doing quick follow-ups. So we had that model with Scribe Agent that we called out, and SRE Agent as well as the Shift Agent. So those will be coming soon, kind of our targeting towards the end of this year to continue that rollout into the MS Teams ecosystem. And but, I'm gonna popcorn to Mya on the next question here. Excuse me.

Which is, when does flexible schedules release? And that's for Mya.

Mya King: Yeah. So, first, I like to share that I talked about flexible schedules and the Shift Agent. The Shift Agent is already GA and the calendar integration, so you can go and begin using that functionality today. Please, go and check that out.

We'd love to hear your feedback. And then in regards to flexible schedules, this is we're gonna be early accessing, early December, mid January. We'll be running a long early access program in December and January. And then GA, we're aiming for is February/March.

So really excited to bring that. Please, go to the PagerDuty early access sign up page and sign up, and I'd be happy to get you participating.

Davis Godbout: Right. So the next one, we're gonna come back to Julia. And around with the SRE Agent, what additional integrations are next in the horizon? Are there any plans to integrate SRE Agent with Datadog, New Relic, or Splunk?

Yeah. Any info on cost and enterprise compatibility?

Julia Nasser: Yeah. So lots of great questions there. So for the integrations, what we support now, we actually do support Datadog already today for logs. So we support Datadog, Grafana, and AWS CloudWatch. We are certainly adding a lot of the top observability players or customers. Some of our many of our customers even have multiple observability tools. So we have many integrations that we're gonna be adding with the top observability players over this next quarter and even beyond that.

So, you know, some of the biggest names there you called out, New Relic, Splunk. Those are certainly on our radar and definitely on our road map, so stay tuned on that.

So that's what we have and what's coming for that. In terms of enterprise grade, I mean, I would say our platform is certainly enterprise grade with PagerDuty. And so all of those integrations that you have, we do have the documentation. I'll drop that in the chat for those interested in terms of how it works and how you might be able to get started for that.

And so I do see a question coming live through the chat, so I'll go ahead and take it on this topic. So any support for the mentioned tools now in the future for GitLab, Elasticsearch, MediaWiki, Microsoft Teams. So I think we took the Microsoft Teams, and we talked about that a minute ago. For other ones, like Elasticsearch, that's definitely on our radar.

GitLab, that's a great one. We actually do support, for example, some change integrations today with GitLab. And in terms of, like, Wiki and Knowledgebase, there are even other tools. We support Confluence today and GitHub, but we are going to be adding more there as well.

So some of the big ones we hear are SharePoint or some, docs in Google. And so all of those are going to be integrations that we continue to look at. So, yeah, absolutely great question.

Davis Godbout: Alright. And back to Mya, around schedules of non calls.

How can PagerDuty manage time zone on call shifts? For example, I would like the support team in Europe on call during their day and shift to on call to US during their day. There is no reason to call someone at 3 AM in US when someone is already actively working at 9 AM in Europe. Mya.

Mya King: Yeah. So this is something we hear a lot from our global customers. And the first step that we're making in flexible schedules is that from a single schedule, we're gonna support the around-the-sun model. So you can have three different rotations or as many rotations as you need, and each of them can have a different time zone so that you could say, this schedule, I want it to be in Lisbon and the Lisbon time zone from eight to five, and then keep going through with each of your teams so that it ensures an easy way for you to create a schedule that is during their working hours.

The next step of that is something that we're evaluating is how to store and ingest, more like timelines, so your working hours and the what's the best time zone for our customers and, like, when they would like to work best. And this is something in the future that we're looking about how we can optimize our schedules for our customers and, would continue like, love talking about this and love to hear more feedback. So please, connect with your teams and and share with them, and we can connect offline on that. Thank you.

Davis Godbout: Alright. So the next question and thank you again. I love the inflow of questions. This is a lot of fun, on the fly. So the next question is back to Julia. And, how mature is the Scribe Agent? Does it update every conversation, or does it summarize the data periodically?

Julia Nasser: Yeah. Great question. So we do include, there's a couple parts there. So as we're transcribing, we do have what we call a catch-me-up functionality.

So in that, the user can, we actually provide those periodic updates based on what's happening in that Zoom bridge. We also have what we call a summary. So whenever the incident's resolved or if the user were to ask for a summary, we're able to pull those key insights from your, again, from your Zoom bridges into that summary as well. So a couple different ways that our Scribe gets information gets to the user as that Zoom bridge or or call is happening.

Davis Godbout: Thank you, Julia. And back to Mya. Does flexible schedules, could also have some override for escalation policy? Example, a responder, you know, is not gonna have multiple EPs but be on vacation, but have a different backup for each escalation policy. Mya?

Mya King: Yeah. So currently, with flexible schedules, our override functionality. When we move away from layered systems, we're gonna be moving to shifts on our schedules, and escalation policies will stay the same and behave and work the same. So we really appreciate this feedback, and this is something as we continue to evolve into escalation policies, we'll consider how we can do overrides. But with the Shift Agent, the Shift Agent looks at the first escalation policy and the schedules that are on there, and it considers that as your on call. And if you come up as on call there, that's something that you can, use the Shift Agent to perform overrides for. So, that's gonna be the first step in ways that you can begin using, like, our agentic functionality to help you with that management.

Yeah. So I'll definitely take that as some feedback and see how we can improve on that.

Davis Godbout: Absolutely.

And so the next question is for Sean. How much support for these tools in regards to being programmable? It's a mouthful. REST API, Python, or other. Sean?

Sean Noble: Yeah. I think I guess I'm gonna take that from the point of view of both our, you know, our MCP as well as interacting with our new advanced agents, our Shift Agent, our SRE Agent, and our, you know, Scribe Agent and so on that you know, our MCP is we have a local version that's open source that you can inspect and, you know, you can install and modify if needed.

And right now, our core MCP features are focused around our incident management use cases like schedules and incidents and, you know, escalation policies, users.

But we are going to be quickly expanding those out to be able to also interact with our advanced tools. So you'll be able to interact with our SRE Agents and the rest of our advanced products through our MCP server. So there's a lot of flexibility to, you know, add a development attachment to all of our PagerDuty advanced tools through whatever your surface or developer ID of choice is.

Let me know if that doesn't answer your question.

Davis Godbout: Alright. And so keep the questions coming. This is like I said, this is a lot of fun to, you know, engage with so many folks with great questions. So I'm gonna tackle the what are the standard security measures HD use to protect data ingested by its agents, like compliance standard access controls?

I'm gonna give you a high level, you know, answer to this, which is, you know, we take our data protection and privacy very seriously. And, you know, we have kind of, we do have rigid controls in which data is accessed in different environments, but, that is top of mind for us and kind of ensuring that, like, one of PagerDuty’s primary premises is reliability and trust with our customers. But, I'll take it as a follow-up, Aaron, as well that we can, send you a message, kind of more details around our policies there.

And happy to speak to our AI, send you some of our AI disclosures.

And then we'll bring this back to Mya, around, not related to AI, but any improvements like a responsive web UI scheduled for the user's page. User onboarding is very dated, limited team name, field length display.

Mya King: This is great, great feedback as well. We're currently focusing on schedules, but we also understand that the user interface and many other pages on our page, needs additional work.

We'll capture this as feedback, but right now, we don't have any plans.

Davis Godbout: Yes. Thank you, Mya, for that. That's and at the core of this, that kind of insight is, you know, the momentum we're making around our champion, the customer improvements. So we'll definitely get that back to the team and, you know, kind of continue refreshing what we’re doing. So I'm gonna come back to Sean around, what's involved in setting up the MCP server. Do we need specific technical expertise, or is it pretty straightforward? Also, are there any prerequisites or dependencies we should know about?

Sean Noble: Yeah. Great question, Davis.

So I'd say that, you know, there are some packages that you need to install when you're using the local server. Those are all detailed on our GitHub repository. If you go to github.com/PagerDuty/pagerduty-mcp-server, you can see all of our installation instructions there. It's, you know, really straightforward if you've ever used Terminal to install anything. If you don't want to play with that, there's, you know, there's one touch installation to Cursor. It's quite easy to set up our remote MCP server to any developer ID or LLM surface of choice.

You know, it's really straightforward and easy to get started with MCP. It's, you know, a very powerful technology from that perspective.

Davis Godbout: Awesome.

So I've got one more question.

Where in the road map is PagerDuty contemplating platform-level enhancements to include more native languages and improved accessibility standards with colleagues who are hard of hearing, visually impaired, etc.

So, I'm gonna give you a little bit of detail here. We are actually, that's I love the enthusiasm around, you know, kind of the customer love improvements, the speed you saw, like, kind of, you know, the evolution of our front end framework. So that is something we're directly investing in right now. And, you know, we've started some early efforts around localization in different places like Japan and, and also in Canada, French language.

So, expect to see more of this, you know, to kind of share with you again. It's like we're working on our underlying foundations and from an internationalization perspective and then applying kind of a rolling localization framework over time. So this is top of mind. We also have an accessibility team as a part of that frontend transformation that we're bringing in accessibility practice across our platform.

So this is going to be an evolving change over time, and, you know, that's something we can also expect to see more from us because that is something that is top of mind from our teams and kind of the experience for all users across, you know, in whatever whatever wherever they may be or however we can help them. So I got one more question here coming in, not related to AI, but do you plan to add HTML markup in the notes section, which is really missing today in limiting the UI experience of the responders. So, actually, we just released a customer log improvement just recently around editing and deleting notes.

So we, that's somewhere we're actually doing some work. With regards to HTML, we haven't explored that point in the short term, but that's great feedback that I can bring back to the team. I think it's especially as we think about the opportunity to bring more data and, you know, that qualitative context from teams, that's a great thing for us to think about in terms of, you know, why that's valuable and how that enhances the overall incident lifecycle and drives into the SRE agent, for example.

And do you have any, let me see. I'm trying to take a handle here on any more questions coming in.

You know, that is the question I have, for Julia is how is the team thinking about integrating SRE agent supporting features within MS Teams given the UI isn't a parallel to Slack?

Julia Nasser: Yeah. Yeah. So as I mentioned, PD Advance already as a base is in MS Team. So we have been in MS Teams in terms of how allowing users to have some of the triage information available in Microsoft Teams.

Obviously, there are differences. So some of our approaches there in terms when we try to move into these channels. Right? So for SRE Agent, thinking about how do we leverage things like threads or how do we have even abilities to, let's say, minimize noise.

So today, for example, our proactive messages actually have toggles where you can enable and disable. So with all those experience, I think, although there are some differences between Slack and MS Teams, there's also a lot of similarities. And so we try to both leverage what we've learned from other ChatOps tools as well as, you know, even already having been in Microsoft Teams.

You heard Scribe is already in Microsoft Teams. So we also try to leverage those components as well. So, yes, all of those experiences, we do look at as we move into Microsoft Teams.

Davis Godbout: Awesome. And so I think we're closing it. I saw one last question around AI Orchestrations. When will that be released? I'll ask you. I'll popcorn that back to you, Julia. I think that's an easy answer, hopefully.

Julia Nasser: Yeah. Absolutely. So AI Orchestrations, I believe it's in, early access now in terms of general availability that's gonna be coming early next year. So that is what we're looking at for AI Orchestrations.

Davis Godbout: Perfect. And that was a nice one to close on.

So alright. It's a great, actually, saw one last question. I love this engagement. I can't stop. I love this. All what was discussed will be supported by Business Enterprise license. Does it require a new plug-in?

So some of the majority of these are base, actually that's kind of a complex question. But, you know, some of the core enhancements, the things that Mya spoke to that's coming up are, you know, the entire platform around, kind of our core AI use cases that is something that we have a, you know, a path for our customers to engage in and then kind of the same thing with our developer experience. So we'll provide more follow-up content around that for you. But, anyways, I'm going to free you all for the rest of your day. Really appreciate all of your time and engagement to ask us great questions, and thank you so much for being partners with us. And we're excited to have, spend some more time with you soon. Have a great rest of your day.