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    AI Agents and the Agentic Loop: Building Autonomous AI Systems

    18 min
    |
    |
    4 de jun. de 2026
    AITechnologyProductivity

    Learn how to build autonomous AI agents using the agentic loop. Explore production-ready frameworks like Vercel AI SDK, LangGraph, and the Claude Agent SDK.

    AI Agents and the Agentic Loop: Building Autonomous AI Systems

    Melhor citação de AI Agents and the Agentic Loop: Building Autonomous AI Systems

    “

    The game has completely shifted from building talkative interfaces to building autonomous agents that actually do things. A chatbot is like a digital tennis partner—you hit a ball, it hits it back—but an AI agent is like a personal assistant: you don't give it a prompt, you give it a goal.

    ”

    Esta aula em áudio foi criada por um membro da comunidade BeFreed

    Pergunta de entrada

    How to build chat bot and ai agents

    Vozes dos apresentadores
    Lenaplay
    Milesplay
    Estilo de aprendizagem
    Profundo
    Fontes de conhecimento
    How to Build an AI Agent in 2026: A Practical Guide  -  Developers Digest
    link
    https://www.developersdigest.tech/blog/how-to-build-ai-agent-2026
    How to Build an AI Agent: Architecture, Tools, and Patterns for 2026 — ChatForest
    link
    https://chatforest.com/guides/how-to-build-ai-agent/
    LangChain vs CrewAI vs AutoGen vs LangGraph (2026 Production Showdown) | HouseofMVPs
    link
    https://houseofmvps.com/blog/ai-agents/ai-agent-frameworks-comparison-2026
    LangChain vs CrewAI vs AutoGen vs LangGraph
    link
    https://automationswitch.com/ai-workflows/langchain-vs-crewai-vs-autogen-vs-langgraph
    LangChain vs CrewAI vs AutoGen: Which AI Framework Wins 2026? - AgileSoftLabs Blog
    link
    https://www.agilesoftlabs.com/blog/2026/03/langchain-vs-crewai-vs-autogen-top-ai

    Perguntas frequentes

    While a chatbot functions like a talkative interface that responds to prompts in a back-and-forth manner, an AI agent acts more like a personal assistant. Instead of just replying to messages, you provide an agent with a specific goal. These autonomous agents are designed to actually perform tasks and move beyond simple message-based interactions to achieve objectives independently.

    The agentic loop is the core cycle that allows an AI to function autonomously. It begins when the AI receives an objective and reasons about the necessary steps. The agent then takes a real action, such as calling an API or reading a file, observes the results of that action, and decides whether to continue the process or if the task is finished.

    Modern AI development has moved past fragile hacks thanks to mature, production-ready platforms. Developers and founders can now utilize powerful frameworks like the Vercel AI SDK, LangGraph, and the Claude Agent SDK. These tools handle the heavy lifting required to build sophisticated agentic workflows and autonomous systems that can reliably execute complex goals in a real-world environment.

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    Pontos-chave

    1

    Section 1: The Loop That Changes Everything

    2

    Section 2: Building the Brain and the Blueprint

    3

    Section 3: Giving the Agent Hands Through Tools

    4:40
    5:08
    5:40
    6:04
    6:25
    4

    Section 4: The Art of the Agentic Loop

    7:23
    8:07
    8:18
    8:25
    5

    Section 5: Memory and Managing the Context Window

    9:46
    9:57
    10:15
    11:14
    6

    Section 6: When One Agent Isn't Enough

    11:20
    12:24
    12:51
    7

    Section 7: Testing the Non-Deterministic

    13:53
    14:37
    8

    Section 8: The Production Playbook

    15:29
    15:59
    16:13
    16:27
    16:44
    16:49
    9

    Section 9: Closing Thoughts and Your Next Step

    17:57
    18:36
    18:45

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