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🖼️🤔🛠️🤖 Context Engineering for Agents

🤖 AI Summary

This video discusses ⚙️ context engineering for agents [00:00].

  • ❓ What is Context Engineering?
    • ✏️ It’s defined as the “delicate art and science of filling the context window with just the right information for the next step” [00:55].
    • 🧠 An analogy is drawn between LLMs and operating systems, where the LLM is the CPU, and the context window is like RAM or working memory with limited capacity [01:01].
    • 🎯 Context engineering is the discipline of deciding what information needs to fit into the context window at each step of an agent’s trajectory [01:15].
  • 🗂️ Types of Context
    • 📚 The video categorizes context into several themes: instructions (prompt engineering), memories, few-shot examples, tool descriptions, knowledge (facts, memories), and tools (feedback from the environment) [01:31].
  • 🤔 Why Context Engineering is Tricky for Agents
    • 🤖 Agents often handle longer or more complex tasks and utilize tool calling [01:58].
    • 📈 These factors lead to larger context utilization, as feedback from tool calls can accumulate, and long-running tasks can consume many tokens over multiple turns [02:12].
  • ⚠️ Problems with Growing Context
    • 📰 The video references a blog post by Drew Brun that outlines specific context failures: context poisoning, distraction, curation, and clash [02:38].
    • 😵‍💫 As context grows, there’s more information for the LLM to process, increasing opportunities for confusion due to conflicting information or hallucinations [02:47].
    • 🚨 Context engineering is critical for agents because they typically handle longer contexts [03:09].
  • 💡 Strategies for Context Engineering
    The video distills approaches into four main categories:
    • ✍️ Writing Context: Saving information outside the context window to help an agent perform a task, such as using a scratchpad for note-taking 📝 or memory for remembering things across sessions [03:56].
    • 🔍 Selecting Context: Selectively pulling relevant information into the context window, including referencing scratchpads, retrieving different types of memories (few-shot examples, facts, instructions), selecting tools, and knowledge retrieval (RAG) [06:08].
    • 🗜️ Compressing Context: Retaining only the most relevant tokens required for a task, often through summarization or trimming [09:58].
    • 📦 Isolating Context: Splitting up context to help an agent perform a task, primarily through multi-agent systems, sandboxing, or organizing context within a state object [11:33].
  • 🧩 How LangGraph Supports Context Engineering
    • 🧱 LangGraph, as a low-level orchestration framework, supports these strategies through its state object for scratchpads and checkpointing [15:14], built-in long-term memory [16:09], utilities for summarizing and trimming message history [18:11], and implementations for multi-agent systems and sandboxes [18:59].

📚 Book Recommendations

🦜 For Large Language Models (LLMs) and their fundamentals:

  • 🏗️ “Build a Large Language Model (From Scratch)” by Sebastian Raschka: 🧑‍💻 This book is ideal for those who want a deep, technical understanding of how LLMs are built from the ground up.
  • 👨‍🏫 “Hands-On Large Language Models” by Jay Alammar: ✍️ Known for his clear explanations and visualizations, Jay Alammar’s books are great for practical understanding and application.
  • 🗣️💻 Natural Language Processing with Transformers by Lewis Tunstall, Leandro von Werra, and Thomas Wolf: 🧠 If you’re interested in the core architecture behind many modern LLMs, this book provides an excellent deep dive into transformers using the Hugging Face library.

✨ For Prompt Engineering:

  • 🪄 “Prompt Engineering for LLMs” by John Berryman and Albert Ziegler: 💡 This O’Reilly book focuses on the art and science of crafting effective prompts to unlock the potential of LLMs. ✍️ It covers architectural understanding, strategy, and specific techniques like few-shot learning and RAG.
  • 🔑 “Unlocking the Secrets of Prompt Engineering: Master the art of creative language generation to accelerate your journey from novice to pro” by Gilbert Mizrahi: 🚀 This book aims to help readers master AI-driven writing and effectively use prompts for diverse applications.
  • 🎨 “Prompt Engineering for Generative AI” by James Phoenix and Mike Taylor: 📚 Another O’Reilly title that provides a solid foundation in generative AI and how to apply prompt engineering principles to work effectively with LLMs and diffusion models.

🤖 For AI Agent Design and Development:

  • 🕵️‍♂️ “AI Agents in Action” by Micheal Lanham: ⚙️ This book offers a practical framework for developing LLM-powered autonomous agents and intelligent assistants. 🧠 It covers knowledge management, memory systems, feedback loops, and multi-agent systems.
  • 🏢 “Building Applications with AI Agents” by Michael Albada: 🔬 This book provides a research-based approach to designing and implementing single- and multi-agent systems, covering core components, design principles, and deployment strategies.
  • 🔗 “LangChain for RAG Beginners: Build Your First Powerful AI GPT Agent” by Karel Hernandez Rodriguez: 🌟 Given the mention of LangGraph in the video, this book focusing on LangChain and Retrieval Augmented Generation (RAG) for building agents would be highly relevant.

🗣️ For Natural Language Processing (NLP) in general:

  • 📜 “Speech and Language Processing” by Daniel Jurafsky and James H. Martin: 👑 Often considered the bible of NLP, this comprehensive book covers a vast range of topics in natural language processing, computational linguistics, and speech recognition.
  • 🐍 “Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit” by Steven Bird, Ewan Klein, and Edward Loper: 👶 A classic for beginners, this book introduces NLP concepts using the NLTK library in Python.

🧠 For Memory Systems in AI:

  • 💾 “AI Memory” by Jamal Hopper: 💡 This e-book specifically explores how artificial intelligence can revolutionize memory retention and learning, examining the intersection of AI, cognitive psychology, and semantics. 🤔 While more conceptual, it touches upon the idea of AI-driven tools enhancing learning and memory, which is relevant to the “memory” aspect of context engineering.
  • 📚 Books that delve into Knowledge Representation and Reasoning in AI would also be beneficial, as effective memory systems often rely on how knowledge is structured and accessed.

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