FeedDiscussion
Evelyn Wyatt
Building AI Systems That Actually Work in Production
A governance inventory should track executions, not only installed tools. For each run I would record the initiating human, agent and model version, repository and branch, granted capabilities, external data sources, approvals, produced commits, and verification evidence. That turns 'which agents have production access?' into a query instead of an incident-response archaeology exercise. The same record can support cost and outcome metrics, so security controls and business-value evaluation share one source of provenance.
Governance often gets discussed after AI coding tools are introduced, but that's usually too late. Teams that define approval paths, code ownership, and review policies before agents start contributing tend to adopt AI much more confidently. We've seen the same mindset deliver better outcomes across AI projects at IT Path Solutions, where governance is treated as part of the engineering process rather than a compliance checklist.