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.

Key Topics:
1. The difference between workflows/chains and real autonomous agents
2. Why React agents fail at complex tasks, parallel execution, and user transparency
3. Free text vs. code-based planning approaches and their trade-offs
4. How planning enables multi-agent systems and model delegation
5. Training planners with reinforcement learning and replanning mechanisms
6. Evaluation challenges: Gaia benchmark, Agent Bench, and building custom datasets
7. Practical advice: When to upgrade from React and which frameworks to use

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.

Building AI agents that actually work is harder than the hype suggests — and most people are doing it wrong. In this special "YAAP: Unplugged" episode (a live panel from AI Tinkerers meetup at the Hugging Face offices in Paris), Yuval sits down with Aymeric Roucher (Project Lead for Agents at Hugging Face) and Niv Granot (Algorithms Group Lead at AI21 Labs) for an unfiltered discussion about the uncomfortable realities of agent development.

Key Topics:

1. Why current benchmarks are broken: From MMLU's limitations to RAG leaderboards that don't reflect real-world performance
2. The tool use illusion: Why 95% accuracy on tool calling benchmarks doesn't mean your agent can actually plan
3. LLM-as-a-judge problems: How evaluation bottlenecks are capping progress compared to verifiable domains like coding
4. Framework: friend or foe? When to ditch LangChain, LlamaIndex, and why minimal implementations often work better
5. The real agent stack: MCP, sandbox environments, and the four essential components you actually need
6. Beyond the hype cycle: From embeddings that can't distinguish positive from negative numbers to what comes after agents

From FIFA World Cup benchmarks that expose retrieval failures to the circular dependency problem with LLM judges, this conversation cuts through the marketing noise to reveal what it really takes to build agents that solve real problems — not just impressive demos.

Warning: Contains unpopular opinions about popular frameworks and uncomfortable truths about the current state of AI agent development.

MCP (Model Context Protocol) is changing how developers connect AI applications to external tools – but what exactly is it, and why should you care? In this episode, Yuval speaks with Etan Grundstein, Technical Product Manager (and formerly Director of Engineering) at AI21, to break down the protocol that’s standardizing AI integrations, moving beyond basic weather APIs and calculators to real-world productivity workflows.

Key Topics:
1) What MCP actually is and how it differs from traditional tool calling
2) Real-world examples: Connecting AI to Jira, Notion, Git, and even Blender
3) The evolution from local MCP servers to cloud integrations
4) Authentication challenges and how they’re being addressed
5) Why developers are building MCP servers to build other MCP servers
6) Looking ahead: Agent-to-Agent protocols and what comes next