When an AI agent takes real actions on company data, being able to reconstruct exactly what it did, when and why is not an optional technical nicety: it is a precondition for trusting the system and for correcting problems precisely.
What a useful log contains
A complete log records not only the action taken but the input that prompted it, the outcome, and the precise moment it happened. Without those elements, tracking down the cause of unexpected behaviour becomes guesswork rather than verification.
Transparency for users, not only developers
A detailed technical log helps whoever builds and maintains the system, but the end user benefits too from an understandable history of the actions taken on their behalf — "I created this quote at 14:32 from this data" — which makes the agent's behaviour checkable rather than a black box.
Value over time, not only in a crisis
A detailed log is not only for investigating a problem after the fact: aggregated over time it lets you analyse usage patterns, spot actions repeated ineffectively, and gradually improve the agent's behaviour from real data rather than general impressions.