A feature taking minutes to build can introduce problems taking engineers hours to investigate.
Google’s 2025 DORA report, based on responses from nearly 5,000 technology professionals, found greater AI adoption was associated with faster software delivery but reduced stability. While researchers did not establish that AI-generated code directly caused more outages, their findings suggest companies produce software faster without necessarily becoming better at maintaining it.
A growing group of companies is working to find out, developing AI systems that investigate failures, identify possible causes, and, in some cases, take corrective action without waiting for an engineer.
“More code, more frequent deployments, and more infrastructure changes naturally create more potential failure points,” Ishay Yaari, cofounder and CEO of DataAgent, told Fast Company.
For site reliability engineering teams, that means managing applications and infrastructure changing more frequently. When something fails, engineers must figure out what changed, identify the cause, and restore service. Consider an application that begins crashing after an update.
Yaari said companies often respond to growing complexity by adding monitoring tools and collecting more data. That helps engineers see what is happening, but also means more alerts to investigate without necessarily improving how quickly problems resolve.
Software monitoring and observability tools traditionally help engineers detect problems and understand causes. Now companies are trying to automate more of the response.
DataAgent is pursuing that approach. Its platform is designed to monitor applications and cloud infrastructure, identify potential issues, and help investigate them. Yaari wants AI to take on operational work engineers currently perform manually.
Some failures have familiar remedies. An application might repeatedly crash because it doesn’t have enough memory. A deployment might fail because of an incorrect configuration. Or a new release might introduce errors that disappear when the system returns to an earlier version.
Companies already use automation to address certain problems through predefined rules. AI could extend that capability by examining circumstances, identifying likely causes, and selecting appropriate responses. The aim is to recognize problems that don’t fit a predefined rule.
Companies adopting AI for operational work appear reluctant to give it unrestricted authority over critical systems. Yaari agrees that some decisions should remain with people.
They also need records of what the agent did, particularly when an attempted fix fails.
Another consideration is the cost of monitoring and observability software. Applications generate vast amounts of logs, performance measurements, and other operational data. Companies pay to collect, store, and analyze that information, often across several platforms. Adding AI can create further expenses if it requires transferring large amounts of data to external services.
Yaari argues that more analysis should happen closer to where applications run.
Its published documentation describes local monitoring alongside cloud-based AI analysis and guided incident investigation. Yaari’s responses describe broader autonomous remediation capabilities.
“The fundamental shift is from people operating infrastructure to people managing autonomous infrastructure,” he says.
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