As AI agents move from experimentation to production, a critical problem emerges: agents can reason and act, but there is no enforceable checkpoint between decision and execution.

What happens when an AI agent can deploy infrastructure, trigger payments, modify records, or execute workflows without a governance control layer?

This session introduces Agent Decision Gate, a governance-enforced control plane designed to sit between AI reasoning and real-world execution.

We’ll explore:

• Why reasoning != authorization
• The architectural gap in most AI agent frameworks
• Designing a policy enforcement checkpoint before action execution
• Multi-agent validation patterns
• Auditability, logging, and enterprise-grade compliance alignment
• How this fits within Microsoft’s AI ecosystem

This is a technical architecture session focused on control, accountability, and system integrity, not prompt engineering.

If you’re building AI systems that act, not just respond, you need a decision gate.