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<title>Agentic Context Engineering</title>
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<h1>I open-sourced Stanford's "Agentic Context Engineering" implementation - agents that learn from execution</h1>
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<p class="intro">With a little help of Claude Code, I shipped an implementation of Stanford's "Agentic Context Engineering" paper: agents that improve by learning from their own execution.</p>
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<p class="section-title">How does it work? A three-agent system (Generator, Reflector, Curator) builds a "playbook" of strategies autonomously:</p>
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<ul class="features">
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<li>Execute task → Reflect on what worked/failed → Curate learned strategies into the playbook</li>
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<li>+10.6% performance improvement on complex agent tasks (according to the papers benchmarks)</li>
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<li>No training data needed</li>
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</ul>
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<p class="integration">My open-source implementation works with any LLM, has LangChain/LlamaIndex/CrewAI integrations, and can be plugged into existing agents in ~10 lines of code.</p>
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<div class="links">
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<p><strong>GitHub:</strong> <a href="https://github.com/kayba-ai/agentic-context-engine" target="_blank">https://github.com/kayba-ai/agentic-context-engine</a></p>
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<p><strong>Paper:</strong> <a href="https://arxiv.org/abs/2510.04618" target="_blank">https://arxiv.org/abs/2510.04618</a></p>
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<p class="feedback">Would love feedback!</p>
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