What is agent observability?
Agent observability is the discipline of knowing what your autonomous AI agents are doing, what they cost, and when they go wrong — before your users (or your bill) tell you.
Why agents need their own observability layer
Traditional APM tools were built for request/response code paths: a user hits an endpoint, your service answers, you measure the latency. AI agents break every assumption in that model. A single user request can fan out into dozens of LLM calls, tool invocations, sub-agent delegations, and retries — all driven by a non-deterministic planner that can decide, mid-flight, to try something nobody on your team has ever seen before.
Agent observability fills that gap. Where APM asks 'how fast was this endpoint?', agent observability asks 'what did this agent decide to do, how many tokens did it burn deciding it, and is it stuck in a loop right now?'
The four things every agent observability tool must answer
If your tool of choice cannot answer these four questions in under thirty seconds, you do not have agent observability — you have logging.
- What is each agent doing right now, and when did it last check in?
- How many tokens and dollars has each agent burned in the last hour, day, and month?
- Is any agent stuck repeating the same action — a loop — that will run my bill into the ground?
- Did anything in the input look like a prompt-injection attempt or a credential leak?
Events, spans, and the agent timeline
Under the hood, agentwach (and any serious observability tool) models an agent's life as a stream of events: started, took an action, called a model, called a tool, finished, errored. Each event carries a timestamp, a type, and a structured payload.
Stitched together, those events form a timeline you can scrub through after the fact — the same way a flight-data recorder lets investigators replay the last six minutes of a flight. When an agent finally does something stupid at 3am, you do not want to be reconstructing what happened from grep. You want to hit play.
Guardrails: observability with teeth
Watching is not enough. Once you can see a runaway agent, you need a way to stop it without paging a human. Guardrails are the enforcement layer: hourly token budgets, daily cost ceilings, loop-detection thresholds. When a limit is breached, the agent gets a structured 'stop' response on its next ingest call and shuts itself down.
This is the difference between an observability tool and an autonomy control plane. agentwach is built to be both.