While automation saves time and boosts productivity, the complexity of developing automation can be a limiting factor and bottleneck. Generative AI is a paradigm shift here, in that it brings consumer-style simplicity to assisting in the development of enterprise-grade automation.

With the new interface of generative AI, organizations can democratize automation and therefore increase the number of individuals that are contributing to authoring and harnessing automation.

To help our customers achieve their goals with automation, we are excited to announce public Early Access for AI-generated Runbooks. Starting today, Runbook Automation users can write the task they wish to automate in plain-English and let AI build a template of automation for that particular task.

AI-generated Runbooks lower the barrier to entry to new automation developers and speeds up the time to create new automation for experienced automation authors. This feature works seamlessly with the user’s preferred scripting language, offering a low-code solution for what used to be a high-code task.

Simply sign up for the PagerDuty Runbook Automation Trial if you are not an existing Runbook Automation user. For existing Runbook Automation customers, App administrators can enable this feature at any time.

Tangible Benefits from Leveraging AI-generated Runbooks

Better Development timelines

Are you a seasoned automation engineer? AI-generated runbooks will help you save time and effort.

Creating self-service automation for tasks involves sifting through documentation, identifying the right calls/commands, and then transposing them into individual job steps manually.
With AI-generated runbooks, authors can generate these on the fly and faster than ever before.

Here’s a quick look at what provisioning access to apps in Okta looks like with and without AI-generated Runbooks:

Faster Onboarding

Get good fast with example jobs for reference. Start with tasks you’re familiar with and see how these tasks operate within the Runbook Automation platform.

Democratize Technical Automation

With AI-generated Runbooks, less experienced automation-authors can quickly go from thought to development to implementation. This broadens the scope of people within an organization that can leverage a technical tool such as Runbook Automation.

Conquering Blank Slate Problems

A typical conundrum for users in the face of automation is “Where do I start?”. With AI-generated runbooks, users can now hit the ground running by creating baseline versions of Jobs for their variety of use cases.

Build with Best Practices for Optimal Results

The AI-generated Runbooks use fine-tuned “prompt engineering” that embeds the known best-practices from the engineers here on the Process Automation team at PagerDuty.
For example, all Jobs are created with a ReadMe that explains the prerequisites for invoking that Job. And all secrets used within the Job are retrieved from Key Storage – rather than requested from the user.

GenAI – Security & Data

A common concern we hear from our customers considering adopting generative AI is around the security of their data, and the potential of giving competitors advantages through model training. PagerDuty AI-generated runbooks feature is an opt-in, meaning you need to enable it to be able to use it.  Furthermore, as stated in the feature documentation:

The only data sent to the generative AI model is the text entered into the prompt field. No other data about your environment, existing Jobs or the source of the prompt is sent to the AI model. Furthermore, the AI model is not trained on the text entered into the prompt.

Read our Guidelines for the Safe and Secure Use of Generative AI to learn more about how we’re working with and building our AI-powered  features.

See AI-generated Runbooks in Action

AI-generated Runbooks propel automation into a new realm, where your operations are empowered like never before. Embrace the future of automation with PagerDuty, and witness the transformation it brings to your operational landscape.

Sign up for the PagerDuty Runbook Automation Trial today!

The post Democratize Automation with AI-Generated Runbooks appeared first on PagerDuty.

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“Existing workload automation strategies are unable to cope with the expansion in complexity of workload types, volumes and locations driven by evolving business demand, as per Gartner. Digital business is slowed without collaboration and automation inside and outside of IT, leading to siloes of capabilities across business and IT teams.Cost optimization is an evolving challenge, driven by technical debt and requirements to demonstrate business value of investments.”

The lack of collaboration and automation both within and beyond IT departments creates isolated capabilities across business and IT teams, hindering the pace of digital business. Additionally, as technical debt accumulates and the need to showcase the value of investments grows, cost optimization becomes an ongoing challenge.

By embracing these key findings in a recent Market Guide published by Gartner®, we believe businesses can streamline their operations and enhance efficiency in the face of expanding complexities. Let’s take a brief look at our understanding of the key findings from Gartner Market Guide for Service Orchestration and Automation Platform.

Workload Automation Challenges

Businesses face a myriad of challenges when it comes to managing workloads effectively. “According to Gartner, existing workload strategies are unable to cope with the expansion in complexity of workload types, volumes, and locations driven by evolving business demands.” Automation strategies that once sufficed are no longer equipped to handle the expanding complexities driven by evolving business demands.

To address these challenges, organizations need to adopt intelligent automation solutions that can adapt to changing requirements. Intelligent workload automation leverages technologies such as artificial intelligence (AI) and machine learning (ML) to dynamically allocate resources, optimize scheduling, and automate repetitive tasks.

Collaboration and Automation: Breaking Silos

According to Gartner, digital business is slowed without collaboration and automation inside and outside of IT, leading to siloes of capabilities across business and IT teams. The lack of collaboration and automation between these teams can significantly hinder digital business initiatives. Silos create barriers, slowing down decision-making processes and impeding the flow of information and ideas.

To overcome these challenges, organizations must foster a culture of collaboration and implement automation solutions that span across business and IT functions. By integrating workflows and sharing information seamlessly, teams can work together in harmony, driving innovation and accelerating digital initiatives. Collaborative automation tools, such as workflow management platforms and project management software, can facilitate effective communication, collaboration, and information sharing, leading to faster time-to-market and improved customer satisfaction.

The Evolving Challenge of Cost Optimization

“According to Gartner, cost optimization is an evolving challenge, driven by technical debt and requirements to demonstrate business value of investments.” As digital infrastructures expand, organizations accumulate technical debt—a burden caused by outdated technologies, inefficient processes, and legacy systems. This technical debt not only impedes agility and innovation but also increases operational costs.

To address this challenge, businesses must prioritize cost optimization through strategic investments and ongoing evaluation of their technology portfolios. Embracing cloud-based solutions, leveraging automation, and adopting agile practices can help organizations reduce technical debt and achieve greater cost efficiency.

Additionally, organizations should establish clear metrics and processes to measure and demonstrate the business value of technology investments, enabling informed decision-making and resource allocation.

Conclusion

We believe the key findings from the report underscore the need for organizations to stay agile, adaptive, and innovative in their approach to workload management, ensuring they can effectively meet evolving business demands and drive sustainable growth in the ever-changing digital landscape.

“According to Gartner, it is recommended to drive collaboration across business and IT teams by democratizing access to automated capabilities through feedback-driven self-service automation solutions.

Unlock the business value of orchestrated I&O automation by implementing expanded service orchestration and event-driven workflows to drive agility, innovation and cost optimization efforts.

Meet today and tomorrow’s business demands by service orchestration and automation platforms (SOAPs) delivering the needed agility and efficiency. Embed agility and efficiency into orchestrated IT processes to meet business demands by using SOAPs.”

To learn how PagerDuty can help you on your automation journey, click here.

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Gartner Market Guide for Service Orchestration and Automation Platforms, Chris Sanderson, Daniel Betts, Hassan Ennaciri, 23 January 2023.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

The post Gartner Market Guide: Embedding Automation Into the Enterprise appeared first on PagerDuty.

This drives a balance between the need to restore a service as quickly as possible, in addition to ensuring enough debug data is available for a later permanent resolution—all while allowing a development team to keep a container running lean and in a performant way.

By capturing both application and environment state when the incident occurs, any responder or service owner spends less time context switching between tools, credentials, and environments—enabling more accurate and faster responses and problem resolution.

The techniques discussed in the prior blogs in this series focussed around modern, cloud-native platforms like Kubernetes, and the unique approaches needed for containers—especially containers that do not natively ship with debugging tools.

Not everyone is able or willing to move every application to cloud native, and many of us still work within a hybrid scenario of both containerized and traditional applications.

Even without the ephemeral nature of containers and the strict policies of container images, there is still a need to capture in-the-moment evidence to help with root-cause analysis in order to avoid future occurrences of incidents.

Let’s look at use cases describing the ability to capture state automatically in the event of a failure or decreased performance, and pick some interesting scenarios to dive into for a deeper look.

This is a non-exhaustive list, but here are some examples of how debug state capture is used in traditional application environments:

Infrastructure & Network

• Top resource-consuming processes on one or more infrastructure components
• TCP dump; thread/memory/core dump

Database

• Top resource consuming queries
• Current query state
• Execution of application specific queries

Application specific

• Java – Run thread/heap dump with tools like jstack
• Windows – Proc Dump
• Python – Running thread dump
• All – Application specific log files

Additional Log Files

Debug state capture can grab whole or partial logs from any file that may not be captured by a log aggregator.

PagerDuty Process Automation provides many pre-built template workflows for capturing application and environment state as part of the automated diagnostics project. These workflows are flexible and extendable so that they can be customized to work for your particular use-cases.

Taking a Deeper Dive

Let us take a closer look at some specific examples of capturing environment state that could prove useful at identifying the long-term fix for an incident.

Use case 1 – Gather database debug

We can use the SQL RUN Step in Process Automation to add either an inline statement, or execute an existing script. As my application is MariaDB (A fork of MySQL), I can use the following parameters to run the MySQL query:

SHOW FULL PROCESSLIST;

(Note: credentials are derived from my existing external store and passed securely as I execute the step as part of a workflow, so I can safely delegate without exposing info)

I pass the output to my Incident platform (In my case, PagerDuty, of course), and set the job to collect automatically if an incident occurs within the database service.

This info is now automatically available to both my responder in their app, chatops tool, or within any post mortem. In this case, I can see someone is running a benchmark test at the point of incident! As with the previous blog posts, it would also be easy to post more complex versions of this to a storage environment like an AWS S3 Bucket for later analysis.

Use case 2 – Gather application debug

My observability tool is very quick to let me know WHEN an application has failed, but not always the information on WHY it failed. This second use case will run an ad hoc command for my python application to use py-spy, a sampling profiler for my application, in conjunction with one of our automation plugins to move files securely to S3 for later retrieval.

Outputs data direct to my S3 storage :

This example highlights worker states for my python app at a thread level, straight into the hands of my developer, and stored for as long as they might need to reference.
Of course, these commands are not exclusive, and I could easily chain multiple checks to provide a broader view.

Use case 3 – Traditional Infrastructure debug state capture

For the third use case, I need to deploy a set of bash commands to a remote machine and run again at the trigger event. This primarily surfaces diagnostics such as open files and network connections, but it also runs bpftrace, a tool that can be used for tracing specific calls:

Process Automation allows me to define and deploy a whole script and store the output for gathering a snapshot of my environment state:

Conclusion

Signals from monitoring tools, even in traditional environments, benefit from broader visibility to allow any responder, DevOps engineer or SRE to make quick and safe decisions. Developers also often need additional information and the ability to capture state when problems arise, as they might not be on hand immediately.

Debug State Capture enables this, providing additional context for a responder, reducing time spent digging around in different tools and the capability to collect deeper datasets for subsequent analysis.

Curious to learn more? Get started today with a trial of Runbook Automation.

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