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 •...
All content for The AI Fundamentalists is the property of Dr. Andrew Clark & Sid Mangalik and is served directly from their servers
with no modification, redirects, or rehosting. The podcast is not affiliated with or endorsed by Podjoint in any way.
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 •...
Mechanism design: Building smarter AI agents from the fundamentals, Part 1
The AI Fundamentalists
37 minutes
3 months ago
Mechanism design: Building smarter AI agents from the fundamentals, Part 1
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 handl...
The AI Fundamentalists
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 •...
