An audit trail should show what the agent wanted to do, who reviewed it, what they saw, what they decided, and what happened next. Stacksona keeps that record with the request before and after the action runs.
Execution status, downstream system response, timestamp sequence, and correlation IDs.
Evidence questions audits should answer
What did the agent intend to do before the side effect occurred?
Which policy rule applied and what decision did it produce?
Who approved or denied the request, and what context did they see?
Did the executed payload match the approved payload?
Why agent logs are not enough
Logs are usually after-the-fact evidence; they do not stop an agent before it sends an email, calls an MCP tool, updates an account, or exports data.
Logs may show a tool was called, but not whether the action was allowed by policy or reviewed by the right human before execution.
Stacksona combines the audit trail with the approval decision so teams can prove what was requested, what was reviewed, and what actually ran.
Operational uses beyond compliance
Investigate incidents without stitching together traces, tickets, and chat messages.
Identify policy gaps by analyzing denied, escalated, and repeated requests.
Measure reviewer load and automation opportunities by action type.
Prove that critical controls operate continuously in production.
Why records matter when agents act
Production AI agents are moving from experiments into support, sales, finance, operations, and regulated workflows. Teams need a clear answer for AI agent audit trails: what gets automated, what gets blocked, what needs human approval, and what evidence is available later.
