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!(#) AI Agent (Artificial Intelligence Agent) is called an intelligent agent. It is essentially a program that automatically executes tasks, with the core being enabling the model to not just answer questions but complete actions step by step. **AI Agent (Artificial Intelligence Agent)** is an intelligent software entity that can perceive the environment, make decisions, and execute actions to achieve specific goals. It is not just a chatbot that answers questions, but an intelligent executor capable of doing hands-on work. Agent = LLM ((Brain) + Planning (Planning) + Tool use (Execute) + Memory (Memory). Quick experience, 0 code, generate an app with one sentence: [https://www.miaoda.cn/](https://www.miaoda.cn/?invitecode=user-93thly701s00). * * * ## Who is this tutorial for? 1. People who want to use AI to automate daily tasks 2. Newcomers who are not familiar with programming but want to use AI for real work 3. People who already know basic computer operations but have zero foundation in concepts like Agent/workflow 4. People who want to elevate AI from chat to actually getting work done * * * ## What is an Agent? An Agent is an intelligent assistant that can get work done. Agent = LLM ((Brain) + Planning (Planning) + Tool use (Execute) + Memory (Memory). Learning Agent requires a mindset shift: evolving from dialogue-based Q&A to goal-driven task execution. !(#) Traditional software programs follow fixed instruction flows: input → processing → output, while AI Agents are more like autonomous employees that can: * **Understand task goals**: Know what result you want * **Make plans**: Think about how to achieve the goal * **Use tools**: Call various resources and APIs * **Self-adjust**: Optimize strategies based on feedback * **Continuously execute**: Until the task is completed or an unsolvable problem is encountered **Analogy:** * Traditional programs = Vending machine: insert coin → press button → get product * AI Agent = Personal assistant: tell requirements → assistant plans → completes task and reports back * * * **Core Structure:** * **Goal:** Know what needs to be accomplished * **Reasoning:** Plan execution steps * **Tools:** Call APIs, code, or systems to complete tasks **Workflow:** Input → Think → Call tools → Execute → Return result → Iterate continuously **Difference from Regular Large Models:** * Large models: Output content * Agent: Output results and drive execution For example, when we chat with an AI Agent and output: "Plan a three-day Beijing trip with a budget of 5000", the intelligent agent will complete the following tasks: * Decompose requirements * Query flights, hotels, and attractions * Generate itinerary * Proceed with bookings when conditions are met !(#) * * * ## Learning Resources Existing platforms and popular frameworks: | Core Needs | Recommended Tools | Key Advantages | | --- | --- | --- | | Miaoda, generate app with one sentence | (https://www.miaoda.cn/?invitecode=user-93thly701s00) | 0 code, generate requirements with one sentence and create the app | | AI Co-pilot, desktop-level AI intelligent agent | (https://www.dumate.cn/?track=aiwebsite_3) | Desktop-level AI intelligent agent for individuals and teams, can see screen, operate software, handle files | | MonkeyCode, AI application development platform | (https://monkeycode-ai.com/?ic=019d94af-c5d0-7207-a923-89d7ccf67d91) | Create tasks directly on the platform, let AI code, use terminal, file management and preview in cloud development environment | | (#), desktop-level AI Agent | (#) | You state requirements, it delivers results. | | Automation triggers and system integration | (https://github.com/n8n-io/n8n) | Wide integration, self-hostable, can connect with regular internal systems | | Deep customization with developer control | (https://github.com/langgenius/dify) / (https://github.com/langchain-ai/langchain) | Former provides complete open-source solution; latter suitable for building complex reasoning chains | | Multi-role collaboration and task decomposition | (https://github.com/microsoft/autogen) / (https://github.com/crewAIInc/crewAI) | Former emphasizes dynamic collaboration; latter drives process with clear role system | | Autonomous task execution Agent | (https://github.com/significant-gravitas/autogpt) | Early phenomenon-level open-source Agent project, emphasizes goal-driven, autonomous task decomposition, loop execution (Plan → Execute → Reflect) | Below are other popular AI Agent open-source frameworks. Most of these projects focus