Verified AI Agents: Constraining Autonomous Systems With Checks They Cannot Skip

This online course addresses the critical challenge of ensuring autonomous AI systems act within defined boundaries while providing verifiable evidence of their actions. Practitioners will learn to implement formal runtime checks that prevent agents from bypassing safety constraints through mathematical proofs and logical verification techniques. The curriculum focuses on action authorization mechanisms that guarantee agents cannot execute prohibited operations, even when faced with adversarial conditions or unexpected inputs. Students will explore methods for creating audit trails that demonstrate actual behavior rather than merely claimed behavior, using formal verification approaches that apply to real-world deployment scenarios.

The hands-on approach enables participants to apply these techniques directly to their own AI systems through practical exercises and guided implementation sessions. Course content covers essential elements including constraint specification, runtime monitoring frameworks, and evidence generation protocols that maintain integrity throughout agent operations. Practitioners will develop skills in creating systems where agents must prove compliance through mathematical validation rather than simple logging. The training emphasizes practical application through coding exercises and scenario-based learning that mirrors actual deployment challenges. Students will leave with concrete tools and methodologies for building verified AI agents that can demonstrate their actions through formal proofs and runtime verification processes.