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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We continue with our series about building agentic AI systems from the ground up and for desired accuracy. In this episode, we explore linear programming and optimization methods that enable reliable decision-making within constraints.

Show notes:

• Linear programming allows us to solve problems with multiple constraints, like finding optimal flights that meet budget requirements
• The Lagrange multiplier method helps find optimal solutions within constraints by reformulating utility functions
• Combinatorial optimization handles discrete choices like selecting specific flights rather than continuous variables
• Dynamic programming techniques break complex problems into manageable subproblems to find solutions efficiently
• Mixed integer programming combines continuous variables (like budget) with discrete choices (like flights)
• Neurosymbolic approaches potentially offer conversational interfaces with the reliability of mathematical solvers
• Unlike pattern-matching LLMs, mathematical optimization guarantees solutions that respect user constraints

Make sure you check out Part 1: Mechanism design and Part 2: Utility functions. In the next episode, we'll pull all of the components from these three episodes to demonstrate a complete travel agent AI implementation with code examples and governance considerations.

What we're reading:

• Burn Book - Kara Swisher, March 2025
• Signal and the Noise - Nate Silver, 2012
• Leadership in Turbulent Times - Doris Kearns Goodwin

What did you think? Let us know.

Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics:

• LinkedIn - Episode summaries, shares of cited articles, and more.
• YouTube - Was it something that we said? Good. Share your favorite quotes.
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The hosts look at utility functions as the mathematical basis for making AI systems. They use the example of a travel agent that doesn’t get tired and can be increased indefinitely to meet increasing customer demand. They also discuss the difference between this structured, economic-based approach with the problems of using large language models for multi-step tasks.

This episode is part 2 of our series about building smarter AI agents from the fundamentals. Listen to Part 1 about mechanism design HERE.

Show notes:

• Discussing the current AI landscape where companies are discovering implementation is harder than anticipated
• Introducing the travel agent use case requiring ingestion, reasoning, execution, and feedback capabilities
• Explaining why LLMs aren't designed for optimization tasks despite their conversational abilities
• Breaking down utility functions from economic theory as a way to quantify user preferences
• Exploring concepts like indifference curves and marginal rates of substitution for preference modeling
• Examining four cases of utility relationships: independent goods, substitutes, complements, and diminishing returns
• Highlighting how mathematical optimization provides explainability and guarantees that LLMs cannot
• Setting up for future episodes that will detail the technical implementation of utility-based agents

Subscribe so that you don't miss the next episode. In part 3, Andrew and Sid will explain linear programming and other optimization techniques to build upon these utility functions and create truly personalized travel experiences.

What did you think? Let us know.

Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics:

• LinkedIn - Episode summaries, shares of cited articles, and more.
• YouTube - Was it something that we said? Good. Share your favorite quotes.
• Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.