How should teams log AI agent decisions without collecting too much sensitive data?
Jax Rivera
Security and legal teams
I am trying to get a realistic read on how should teams log AI agent decisions without collecting too much sensitive data.
Balance auditability, privacy, retention, and debugging needs.
What has actually worked (or failed) for your team? Specific examples, pricing traps, or vendor claims that did not hold up are especially useful.
Leo Park
Customer success lead
We compared two vendors on the same 20 tickets. Accuracy was fine; escalation quality was not. Score human handoff and confidence thresholds harder than model branding.
Imani Brooks
Strategy & architecture
Document what 'done' means for the workflow. We shipped an agent that 'worked' but still required a human to close the loop every time — zero net time saved.
Felix Kron
RevOps practitioner
If you are non-technical, demand a sandbox with sample data and a 30-minute setup path. Anything that needs a solutions engineer for the first win will stall on a small team.
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