Tag:Observability

A ReAct-style agent calls the same search tool, gets the same unhelpful result, decides another identical call will help, and does it again. Seventy times. It never crashes and never finishes, it just burns tokens going nowhere until a timeout or your bill catches it. A hard step ceiling is a backstop, not a fix. What you want is a guard that notices the agent has stopped making progress and breaks the cycle in seconds.

A user sends you a screenshot of your agent doing something wrong. You have the logs, you have the trace, and you still cannot make it happen again, because the model sampled differently and a tool returned something new. You are debugging a ghost. Record every source of nondeterminism during the run and you can replay that exact failure on demand.

An agent's cost is elastic and path-dependent, so the same task runs for two dollars one day and forty the next. Plain logs will not tell you which step looped. A distributed trace built on OpenTelemetry's GenAI conventions shows you exactly where the tokens went.