Sid Mangalik and Andrew Clark explore the unique governance challenges of agentic AI systems, highlighting the compounding error rates, security risks, and hidden costs that organizations must address when implementing multi-step AI processes.

Show notes:

• Agentic AI systems require governance at every step: perception, reasoning, action, and learning
• Error rates compound dramatically in multi-step processes - a 90% accurate model per step becomes only 65% accurate over four steps
• Two-way information flow creates new security and confidentiality vulnerabilities. For example, targeted prompting to improve awareness comes at the cost of performance. (arXiv, May 24, 2025)
• Traditional governance approaches are insufficient for the complexity of agentic systems
• Organizations must implement granular monitoring, logging, and validation for each component
• Human-in-the-loop oversight is not a substitute for robust governance frameworks
• The true cost of agentic systems includes governance overhead, monitoring tools, and human expertise

Make sure you check out Part 1: Mechanism design, Part 2: Utility functions, and Part 3: Linear programming. If you're building agentic AI systems, we'd love to hear your questions and experiences. Contact us.

What we're reading:

We took reading "break" this episode to celebrate Sid! This month, he successfully defended his Ph.D. Thesis on "Psychological Health and Belief Measurement at Scale Through Language." Say congrats!>>

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AI governance: Building smarter AI agents from the fundamentals, part 4