When an AI triage agent falls short on a hard incident, it’s usually in one of three ways. It’s slow, because much of its time goes to relearning your environment. It’s vague, because nobody told it what the answer should look like. Or it’s confidently wrong, because nothing checked the answer before you acted on it. Each has its own fix, and none of them is waiting on the next model. ...
Agent autonomy is usually pictured as a ladder — a low rung where the agent does a little on its own, a high rung where it runs nearly unattended, and the job is to climb. There’s a sharper way to read the same progression. Each step up isn’t more autonomy in some general sense; it’s one specific decision moving out of human judgment, or out of code, and into the agent. ...
A cheap classifier should pay for the expensive investigator. Three stages — deterministic pre-filter, lightweight classify, full triage — let the cost shape match the value shape on a queue of mixed-value events. Spend tokens where the work is. The easy way to build an agent on a queue is one prompt that does everything: receive an event, decide if it matters, pull the data, write the response. One prompt, one model call, simple to ship. ...
The same triage agent running on your laptop and running server-side off an alert webhook looks like the same agent — but how much agency you give the LLM should be completely different on each side. Locally, you should let the LLM stretch out: hand it the MCPs you’ve set up for investigation, let it pick which tool to call, let it choose its own path, let it backtrack and try a different hypothesis. Discovery is a feature. You’re standing right there — every step shows up on your screen, and you can redirect at any time. ...