An effective observability layer captures four categories of data. First, inputs — what the human asked Claude to do, including the full prompt context and any attached documents. Second, outputs — what Claude produced, including intermediate reasoning steps where available. Third, connector activity — which systems were accessed, what data was read or written, and what operations were performed. Fourth, decisions — the chain of logic Claude followed to arrive at its output.
The full chain of events
Capturing all four categories gives you a complete picture of any AI-assisted workflow. When a question arises — why did Claude draft that email? What data did it pull from the CRM? Who asked it to update that record? — the answers are in the audit trail. You can trace any outcome back through the full chain of events to the original human request.
The practical implementation depends on the scale and sensitivity of the deployment. Some businesses capture everything. Others focus on connector activity and decision points. The important thing is that the capture layer exists from day one — retrofitting audit trails onto a running deployment is significantly harder than building them in from the start.