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    Giving AI Agents Hands: Designing Custom Tools for LLMs

    17 min
    |
    |
    9 de jun. de 2026
    TechnologyProductivity

    Learn how to give AI agents hands by designing custom tools. Explore the architecture of tool use, including schemas and functions, to move beyond chatbots.

    Giving AI Agents Hands: Designing Custom Tools for LLMs

    Melhor citação de Giving AI Agents Hands: Designing Custom Tools for LLMs

    “

    A model without tools is essentially a prisoner in a room full of books—it can reason and plan with breathtaking depth, but it simply cannot act.

    ”
    T

    Generated by T

    Pergunta de entrada

    Building AI agents and workflows with a specific focus on designing and implementing custom tools, and how to integrate them with files and prompts.

    Vozes dos apresentadores
    Lenaplay
    Fontes de conhecimento
    Building Managed Agents  |  Gemini API  |  Google AI for Developers
    link
    https://ai.google.dev/gemini-api/docs/custom-agents
    OpenAI Agents SDK
    link
    https://openai.github.io/openai-agents-python/
    Tools - OpenAI Agents SDK
    link
    https://openai.github.io/openai-agents-python/tools/
    Writing effective tools for AI agents—using AI agents - Anthropic
    link
    https://www.anthropic.com/engineering/writing-tools-for-agents
    AI Letters #06 · Give Your Agent Hands: Tools, APIs, and the Model Context Protocol · Part 2 of 5 - Build Agents, Not Chatbots Series
    link
    https://engineersofai.substack.com/p/ai-letters-06-give-your-agent-hands
    Files in Workflows - Pathlit
    link
    https://pathlit.mintlify.app/docs/guides/files-in-workflows

    Perguntas frequentes

    Giving an AI agent hands refers to the process of designing custom tools that allow a Large Language Model to move beyond reasoning and start taking action. Without these tools, an agent is like a strategist who can plan a complex task but cannot physically interact with the world. By integrating custom tools, you enable the agent to reach out and touch files or APIs, transforming a mere chatbot into a functional agent capable of executing tasks.

    The architecture of giving an AI agent agency relies on a three-part contract consisting of a schema, a function, and a result. The schema is particularly vital because it serves as the digital label that the AI actually sees, allowing it to understand how to use the tool. This structure ensures that the model can move from theorizing about a problem to actually performing the necessary function and receiving a specific result from the system.

    Schema design is the most critical piece of the tool-building process because the schema is the only part of the tool that the AI model actually perceives. It acts as the interface or label on the tool's handle, providing the necessary context for the agent to understand when and how to trigger a specific function. Without a well-designed schema, an agent might reason with depth but will fail to correctly identify or utilize the tools needed to interact with external APIs.

    Standard LLM reasoning allows a model to plan, calculate, and theorize with breathtaking depth, but it remains a prisoner to its training data without the ability to act. Custom tools provide the physical hands necessary for an agent to perform real-world tasks like pulling deployment logs or organizing a calendar. While a model can narrate how it would solve a problem, custom tools and function calling are what allow it to actually execute those plans in a digital environment.

    Criado por ex-alunos da Universidade de Columbia em San Francisco

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    "Reading used to feel like a chore. Now it’s just part of my lifestyle."

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    "Feels effortless compared to reading. I’ve finished 6 books this month already."

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    Pontos-chave

    1

    The Ghost in the Machine Needs a Grip

    0:00
    0:55
    2

    The Architecture of Agency and the Power of the Registry

    2:09
    2:52
    3:40
    3

    Designing Tools That Don't Break the Reality

    4:22
    5:15
    6:11
    4

    The File System as a Shared Workspace

    6:56
    7:31
    8:11
    5

    Mastering the Model Context Protocol and Standards

    8:55
    9:31
    10:20
    6

    Moving Files Between the Nodes of a Workflow

    10:55
    11:42
    12:20
    7

    The Practical Playbook for Building Custom Tools

    13:07
    13:47
    14:35
    8

    Closing Reflections on the Evolution of Agency

    15:15
    15:52
    16:25

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