An AI agent's proposed action needs a decision before it becomes a real-world side effect. This session introduces Pollard, a runtime for governing execution at that boundary while leaving the application's model client, tools, prompts, and agent loop under the developer's control.

We examine how explicit limits, registered tool actions, and an auditable execution tree help an agent refuse an inadmissible step, pause for human oversight, and preserve evidence of what happened. Recorded results support replay and testing; live revalidation checks for observable changes without replacing the original record.

The session gives developers a practical way to reason about agent control, auditability, and the limits of replay. Attendees will see where these controls belong in an application and how to distinguish a verifiable record from a guarantee about the outside world.

Pollard source and documentation: https://github.com/jemsbhai/pollard

Audience: developers and technical leaders building AI agents.