<p>Everyone's talking about AI agents, but most of what we call "agents" are just workflows in disguise. Real autonomous agents require planning. And that, changes everything. In this episode, Yuval speaks with AI21's Algo Tech Lead, Nitzan Cohen about why the popular React framework isn't enough and how planning architecture unlocks true agent capabilities.</p><p>Key Topics:<br>1. The difference between workflows/chains and real autonomous agents<br>2. Why React agents fail at complex tasks, parallel execution, and user transparency<br>3. Free text vs. code-based planning approaches and their trade-offs<br>4. How planning enables multi-agent systems and model delegation<br>5. Training planners with reinforcement learning and replanning mechanisms<br>6. Evaluation challenges: Gaia benchmark, Agent Bench, and building custom datasets<br>7. Practical advice: When to upgrade from React and which frameworks to use</p><p>From competitive analysis that runs in parallel to breaking down complex coding tasks, discover how planning transforms AI agents from simple tool-calling loops into sophisticated problem-solving systems.</p>