One meta-coding area where I have found AI provides real value is in log examination. When a problem occurs, the first question that usually gets asked is, “Where is the log of that happening?” Back in the before times, you’d have to pore over the log, line by line, searching for exactly what happened for clues into the source of the problem. But now? Give the log, however large, to an AI agent, and those answers appear in a matter of minutes.
Producing the log becomes the real value—displaying dashboards over that data becomes less important. A tool like Datadog owns the ingestion pipeline and the time-series production, and it creates valuable data, so its pivot is easier. Datadog need only create a tool that talks to an AI agent instead of a human. Their beachhead is solid. The real value of logs lies in an agent’s ability to peer into them in real time and take action based on what it sees. It won’t be long until, whenever a problem occurs, an MCP server will notify an AI agent and the agent will analyze the problem, fix it, and deploy the fix, all without human intervention.
Producing and owning the data beats being able to interpret the data. Tools that produce the data can lean into the AI revolution. Tools that merely read and display data from a different source—say, an existing repository—will have a much harder time surviving alongside AI agents.
