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Does Your AI Framework Change the Output?
When you build an AI agent pipeline, does the framework you choose actually change what comes out the other end? This episode explores a real-world case: a multi-agent security report generator for Israel, built with LangGraph. We compare it against two alternatives — Deep Agents and Pydantic — using identical models, tools, and prompts. The surprising finding: the harness itself shapes the output in fundamental ways, from stopping conditions to output structure to how domain expertise gets encoded. We discuss why LangGraph's graph-based approach lets you embed real judgment, why Deep Agents…
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