Building an AI agent demo takes an afternoon. Deploying it safely to production takes months — and that gap is almost entirely a governance problem, not a model problem. This technical deep dive presents a four-pillar framework — Lifecycle Management, Risk Management, Security, and Observability — that gives engineering teams a concrete, implementable path from prototype to production-ready agent system.

The talk covers nine actionable patterns: versioning agents as deployable artifacts with CI/CD and eval gates; securing data access through curated views, column masking, and intentional APIs; assigning dedicated least-privilege service identities; and building governance-grade observability that
can answer, for any request, which version ran, which tools were called, and whether policy was followed.

Attendees leave with a production readiness checklist, a prioritized four-step implementation roadmap, and a framework they can apply immediately to any agent they have in production or in development.