Enterprises are awash with AI agent prototypes—but few make it past the pilot phase. The gap isn’t ambition; it’s architecture, governance, and trust. This session presents a structured, framework-driven playbook for moving from experimentation to execution.

We’ll introduce a two-layer decision framework that connects reasoning strategies (how agents think) with architectural patterns (how they’re built), enabling teams to match the right design to their risk tolerance, data maturity, and operational context. Alongside it, we’ll unpack the four non-negotiable pillars of production-grade agents:

Data Integrity & Lineage – ensuring every response is grounded in authoritative sources.

Hallucination Prevention – using validation and grounding to guarantee reliability.

Compliance & Auditability – embedding controls and traceability by design.

User Trust & Adoption – achieving confidence through transparent automation and human oversight.

The session outlines concrete metrics, testing workflows, and phased deployment models that emphasize augmentation over replacement. Attendees will leave with actionable frameworks to help their AI agents graduate from lab experiments to enterprise assets.