How AI Agents Will Transform DevOps Workflows for Engineers

For engineers, not all AI is equal.

The surge of large language models (LLMs) has opened up new opportunities for engineers to increase their productivity. LLMs have proven capable of doing a decent job at generating boilerplate code, unit tests and code documentation. However, LLMs alone lack reasoning capabilities and contextual understanding to solve more complex DevOps tasks and have a bigger impact on development workflows.

But AI has evolved beyond traditional chatbots and content generation. AI agents represent the next step toward dynamic, intelligent assistants that aren’t simply used to generate code. Instead, AI agents offer the potential for organizations to significantly increase engineers’ productivity and efficiency during DevOps tasks. Beyond gains in cost efficiency and potentially reducing errors, deploying AI agents across operations management will give time back to engineers, allowing them to focus on driving innovation instead of repeatedly fighting the same fires.

How Do AI Agents Differ from Generative AI?

There are five key considerations for an organization when adopting AI agents: use cases, human input, autonomy, decision-making ability and adaptability.

The generative AI tools engineers are using today can create content such as text summaries and code. These tools require direct human input to generate outcomes, are fully reliant on prompts and are unable to make independent decisions. Once these models have generated an output, they require further prompts from a human to refine their outputs.

On the other hand, AI agents can act independently to analyze, plan and execute tasks. They reduce the need for human interaction to get to an outcome, have a higher level of autonomy and are able to act based on short- and long-term memory and predetermined objectives.

AI agents are capable of adapting to the information extracted from different tools and deliver relevant results, even when circumstances change.

The ability to operate independently and learn from individual scenarios makes AI agents the perfect evolution in DevOps operations for engineers, accelerating productivity and allowing them to focus on high-priority innovation and business-value tasks.

Reducing Unplanned Work and Increasing Productivity

In the context of both IT and DevOps operations, time is money. The cost of hourly downtime can exceed $300,000 for organizations, so any tools that can accelerate incident remediation and reduce maintenance time are highly valuable. AI provides an opportunity to significantly improve metrics such as mean time to repair (MTTR) by not only intelligently automating incident management efforts, but also helping organizations to become more preventive. By implementing AI agents, organizations can remove work from incident responders, minimize alert fatigue and instead autonomously resolve known and recurring issues as they arise. Additionally, as we enter this new era of experience, AI agents can provide recommendations and act based on learnings from past issues.

AI agents can also help resolve challenges related to service ownership. While this approach provides clarity around the designated owners of code, it also forces engineers to be available to own their code through its entire life cycle, which means that every time there is an issue, they are called on to fix it. This can be as simple as closing memory leaks, fixing a logic error or repairing a misconfigured API request. Repetitive tasks such as these are perfect for AI agents, which can learn from historic actions to automate common resolutions, such as a rollback to a stable version.

Additionally, AI agents can help engineers with their CI/CD pipeline, reviewing code, acting as a sounding board and allowing them to make smarter, data-driven decisions, resolve critical issues more quickly and focus on business priorities. These productivity and efficiency gains benefit team leads and managers up the hierarchical chain due to a reduction in unplanned work, operational costs, time-critical situations, increased ROI and focus on innovation.

Operations Management Use Cases for AI Agents

AI agents are still at an early stage of adoption, but first movers and early adopters have already begun to use them across their DevOps operations pipeline.

AI agents can be used to support organizations with their planning, which can take the form of a scheduling agent that can preempt scheduling and availability conflicts by adjusting on-call shifts in real time, based on each team member’s availability. As a result, responders can be available and on call for significant incidents, without being interrupted out of hours. This more robust schedule will reduce burnout and ensure engineers’ workload is effectively managed.

AI agents can also be used to connect disparate data across multiple sources within an organization’s entire technology stack. For example, they can connect information found in runbooks, documents and internal developer platforms, often needed during unplanned and planned work. By autonomously identifying patterns in this data, AI agents offer solutions to strategic operational decisions and continuously improve operational and business efficiency.

AI agents can also operate in a function similar to experts, such as a site reliability engineer. These types of AI agents can support human responders by surfacing important context about the state of your infrastructure or your network and find common remediation solutions related to past issues, as well as guide responders with recommendations to accelerate diagnosis and remediation, helping to further reduce the business risk caused by a disruption.

AI Agents Are the Ultimate Tools for Engineers

Engineers are vital to an organization’s ability to deliver exceptional customer experiences. They build the products and capabilities that attract customers and, ultimately, create revenue. However, they are often still burdened with repeating the same action over and over every time an application they are responsible for goes down, wasting time and leading to siloed knowledge, forcing the same engineers to resolve the same incidents.

Given how wasteful these processes can be, AI agents offer an entirely way that organizations can operate. AI agents promise to remove the toil from the engineer role and empower them to get back to the creative work that brings customers and revenue to their organization.