AI Is for DevOps: How a Neural Network Debugs Failed Pipelines

How often does someone rush to you wide-eyed, begging for help with a broken pipeline? Or you find yourself staring at a red status in Slack on a Friday evening, knowing the next 15–20 minutes will be spent on routine work: open the log, find the error line, compare with the last commit, check dependencies… The work is straightforward. And that's exactly why it's boring — a perfect candidate for automation. Fortunately, neural networks can now handle this for us and provide solid advice (not all of them, but some definitely can). What to Think Through Beforehand Before writing any code, it's worth answering four questions. They'll define the architecture of the entire solution. What events trigger the analysis? In our case — a job that finished unsuccessfully in CI/CD. To start diagnostics, it's enough to pass the agent the last 50 lines of the build log and the pipeline file contents. What data sources will be needed? The main ones are the version control system (repository access), C