These days, it almost feels like old news: Developers can now use AI tools to generate code, build features, and make changes in a fraction of the time they once needed. The problem now surfacing across businesses is that once software goes into production, teams still need to monitor, maintain, and fix it. A feature that takes minutes to build can introduce problems that take engineers hours to investigate.
Google’s 2025 DORA report, based on responses from nearly 5,000 technology professionals, found that greater AI adoption was associated with faster software delivery but reduced stability. While the researchers didn’t establish that AI-generated code directly caused more outages, their findings suggest that companies can produce software faster without necessarily becoming better at maintaining it.
That raises a critical question for companies investing heavily in AI coding tools: If development keeps getting faster, how much of the work involved in running software can also be automated? A growing group of companies is working to find out, developing AI systems that can investigate failures, identify possible causes, and, in some cases, take corrective action without waiting for an engineer.
Ishay Yaari, cofounder and CEO of DataAgent, a company developing autonomous remediation software, says the operational side of software development hasn’t advanced at the same pace as coding.
“More code, more frequent deployments, and more infrastructure changes naturally create more potential failure points,” he tells Fast Company.
For site reliability engineering (SRE) teams, that means managing applications and infrastructure that may be 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. The cause could be faulty code, an incorrect configuration, insufficient computing resources, or a problem with another service. While monitoring tools may detect the failure immediately, engineers still need to examine logs, review recent changes, and investigate several possibilities before deciding what to fix.
Yaari says companies often respond to growing complexity by adding monitoring tools and collecting more data. That can help engineers see what’s happening, but it can also mean more alerts to investigate without necessarily improving how quickly problems are resolved.
The pressure isn’t necessarily coming from poor-quality AI-generated code. Even reliable code can be difficult to manage when applications, infrastructure, and dependencies are changing frequently.
In a recent analysis of AI-assisted development, Google’s DORA researchers found that developers saved time generating code but spent more time reviewing and verifying it. They also emphasized the importance of testing, feedback, and established engineering practices in turning faster development into better results.
For companies, that means the benefits of AI coding tools may depend partly on what happens after developers finish writing the code, particularly the ability of SRE teams to keep applications running reliably. Software monitoring and observability tools have traditionally helped engineers detect problems and understand their 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 more of the 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 the circumstances, identifying likely causes, and selecting an appropriate response.
Yaari says DataAgent’s platform is designed to understand both the infrastructure supporting an application and the application itself, including how services interact, what the application is expected to do, and how its behavior changes after an update.
The aim is to recognize problems that don’t fit a predefined rule. But identifying a likely solution is one thing. Allowing software to make changes to a live application is another.
Consider a deployment that introduces errors. Rolling back to the previous version may restore service, but if the update also changed a database or introduced new dependencies, reversing it could create additional problems. That’s why the ability to act matters as much as the ability to diagnose.
In its 2026 State of AI-First Operations report, PagerDuty found that 44% of surveyed organizations required human involvement when AI was used to fix problems affecting customer-facing systems, while 43% required it when coordinating incident responses across teams.
Even 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. He points to actions such as deleting large amounts of data, disabling security controls, or exposing sensitive information as examples that should require approval.
“The goal is not to remove humans from SRE; it is to remove humans from the repetitive operational work that machines can handle safely,” he says.
Companies must determine which systems an AI agent can access, what changes it can make, and when it must request approval. 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. DataAgent is pursuing a distributed approach, with agents deployed within customers’ infrastructure to reduce the amount of data sent elsewhere.
Its published documentation describes local monitoring alongside cloud-based AI analysis and guided incident investigation. Yaari’s responses describe broader autonomous remediation capabilities. The company has not provided independent performance data establishing how much operational work or cost those capabilities can eliminate.
Yaari expects engineers to spend less time investigating repetitive incidents and more time designing systems, setting operational policies, and deciding what automated tools should be allowed to do.
“The fundamental shift is from people operating infrastructure to people managing autonomous infrastructure,” he says.
That could allow smaller engineering teams to support more applications, but it would also require them to understand how automated systems make decisions—and what happens when those decisions are wrong.
Engineering skills may evolve as a result. System architecture, security, reliability, and the ability to investigate unusual failures could become more important as routine operational work becomes easier to automate.
Google’s DORA research found that organizations with strong engineering practices were better positioned to benefit from AI-assisted development. The same lesson may apply to AI-powered operations. Companies will still need reliable processes for testing changes, responding to incidents, and evaluating whether automated fixes work.
The software industry has made considerable progress in helping developers produce code faster. Now it’s trying to bring some of that speed to keeping applications running.
If AI eventually handles both, engineers may spend less time performing individual tasks and more time deciding what the systems they build should do, how they should behave, and when a person needs to intervene.