Agents need observability before they deserve autonomy
Production AI agents fail silently, expensively, and weirdly. Before granting autonomy, build the trace: every tool call, every decision point, every dollar, replayable.
The gap between an agent demo and an agent in production is accountability. In the demo, a human watches every step. In production, the agent runs at 3 a.m. against real data with real permissions — and when something goes wrong, 'what did it actually do?' becomes a forensic investigation across scattered logs.
The minimum viable trace
- Every model turn with its full input context — not just the last message
- Every tool call with arguments, result, latency, and side effects
- Every decision point where the agent chose between actions, with the alternatives it rejected
- Cost per step, aggregated per task — agents fail financially before they fail functionally
With that trace, an agent incident is a replay, not an interrogation. You can pinpoint the turn where context went wrong, measure how often a tool misfires, and set alerts when behavior or spend departs from baseline — the same discipline SRE brought to services, applied to autonomous software.
Why agent traces are too sensitive to rent
An agent trace is a recording of your business logic executing: the prompts that encode your processes, the data your tools touch, and the decisions made on your behalf. For sensitive systems, the trace store belongs inside the agreed data boundary, next to the agents it observes, with retention and access rules the customer can inspect.
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