What if we've been approaching AI agents all wrong? While the tech world obsesses over larger language models (LLMs) and prompt engineering, there'a a foundational approach that could revolutionize how we build trustworthy AI systems: mechanism design.

This episode kicks off an exciting series where we're building AI agents "the hard way"—using principles from game theory and microeconomics to create systems with predictable, governable behavior. Rather than hoping an LLM can magically handle complex multi-step processes like booking travel, Sid and Andrew explore how to design the rules of the game so that even self-interested agents produce optimal outcomes.

Drawing from our conversation with Dr. Michael Zargum (Episode 32), we break down why LLM-based agents struggle with transparency and governance. The "surface area" for errors expands dramatically when you can't explain how decisions are made across multiple steps. Instead, mechanism design creates clear states with defined optimization parameters at each stage—making the entire system more reliable and accountable.

We explore the famous Prisoner's Dilemma to illustrate how individual incentives can work against collective benefits without proper system design. Then we introduce the Vickrey-Clark-Groves mechanism, which ensures AI agents truthfully reveal preferences and actively participate in multi-step processes—critical properties for enterprise applications.

Beyond technical advantages, this approach offers something profound: a way to preserve humanity in increasingly automated systems. By explicitly designing for values, fairness, and social welfare, we're not just building better agents—we're ensuring AI serves human needs rather than replacing human thought.

Subscribe now to follow our journey as we build an agentic travel system from first principles, applying these concepts to real business challenges. Have questions about mechanism design for AI? Send them our way for future episodes!

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