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 requirementsThe Lagrange multiplier method helps find optimal solutions within constraints by reformulating utility functionsCombinatorial optimization handles discrete choices like selecting specific flights rather than continuous variablesDynamic programming techniques break complex problems into manageable subproblems to find solutions efficientlyMixed integer programming combines continuous variables (like budget) with discrete choices (like flights)Neurosymbolic approaches potentially offer conversational interfaces with the reliability of mathematical solversUnlike pattern-matching LLMs, mathematical optimization guarantees solutions that respect user constraintsMake 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 2025Signal and the Noise - Nate Silver, 2012Leadership in Turbulent Times - Doris Kearns GoodwinWhat 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.

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