AI Agent (Intelligent Agent) Tutorial
AI Agent (Artificial Intelligence Agent) is also called an intelligent agent. In essence, it is a program that automatically executes tasks. The core idea is to let the model not only answer questions but also complete actions step by step.
AI Agent (Artificial Intelligence Agent)It 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 a smart executor that can actually do things.
Agent = LLM (Brain) + Planning (Planning) + Tool use (Execution) + Memory (Memory).
Quick experience: 0 code, generate an app with one sentence:https://www.miaoda.cn/。
Who is this tutorial for?
- People who want to use AI to automate daily tasks
- Beginners unfamiliar with programming but who want to use AI for real work
- People who already know basic computer operations but have zero foundation in concepts like Agent/Workflow
- People who want to elevate AI from chatting to actually doing work
What is an Agent?
An Agent is an intelligent assistant that can get things done.
Agent = LLM (Brain) + Planning (Planning) + Tool use (Execution) + Memory (Memory).
Learning Agent requires a mindset shift: fromchatbox Q&Aevolve togoal-driven task execution。
Traditional software programs follow a fixed instruction flow:Input → Process → Output, while an AI Agent is more like an autonomousemployee, it can:
- Understand task goals: understand what result you want
- Make a plan: think about how to achieve the goal
- Use tools: call various resources and APIs
- Self-adjust: optimize strategies based on feedback
- Keep executing: until the task is completed or an unsolvable problem is encountered
Analogy for understanding:
- Traditional program = vending machine: insert coin → press button → get product
- AI Agent = Personal Assistant: Tell it your needs → Assistant plans → Complete tasks and report back
Core structure:
- Goal:Knows what needs to be accomplished
- Reasoning:Plans execution steps
- Tools:Calls APIs, code, or systems to complete tasks
Workflow:
Input → Think → Call tool → Execute → Return result → Iterate continuously
Differences from regular large language models:
- LLM: Outputs content
- Agent: Outputs results and drives execution
For example, when we talk to an AI Agent and input:Plan a 3-day Beijing trip with a budget of 5000, the agent will complete the following tasks:
- Break down the requirements
- Search for flights, hotels, and attractions
- Generate an itinerary plan
- Complete bookings when conditions are met
Learning Resources
Existing platforms and popular frameworks:
| Core needs | Recommended tools | Key advantages |
|---|---|---|
| Miaoda: Generate apps with a single sentence | Miaoda official website | Zero code; generate apps from a single sentence describing your needs |
| MonkeyCode: AI application development platform | MonkeyCode official website | Create tasks directly on the platform, let AI code, and use terminal, file management, and preview in the cloud development environment |
| Xiaoyunque: CapCut's AI video generation | CapCut - Xiaoyunque | ByteDance's self-developed Seedance 2.0 video model + Seedream 5.0 image model, paired with Doubao LLM for copy understanding |
| QoderWork, desktop-level AI Agent | QoderWork | You state your needs, it delivers results. |
| Automated triggers and system integration | n8n | Wide integration coverage, self-hostable, can connect to common internal systems |
| Deep customization controlled by developers |
Dify LangChain |
The former provides a complete open-source solution; the latter is suitable for building complex reasoning chains |
| Multi-role collaboration and task decomposition | AutoGen CrewAI |
The former emphasizes dynamic collaboration; the latter drives workflows with a clear role system |
| Autonomous task-executing Agent | AutoGPT | An early phenomenon-level open-source Agent project, emphasizing goal-driven, autonomous task decomposition, and loop execution (Plan → Execute → Reflect) |
The following are other popular open-source AI Agent frameworks. Most of these projects revolve around tool calling (Tool Calling), memory (Memory), workflow (Workflow), multi-Agent collaboration (Multi-Agent), and long-term task execution capabilities.
| Project | Positioning | Features |
|---|---|---|
| OpenAI Agents SDK | Lightweight Agent development framework | Supports tool calling, handoff (Handoff), multi-Agent orchestration, simple structure, quick to get started |
| LangGraph | State machine-based Agent orchestration | Controls complex processes based on graph structure, supports long-term state and loop execution |
| LlamaIndex | RAG + Agent framework | Specializes in knowledge base, data connection, and retrieval-augmented scenarios |
| Semantic Kernel | Enterprise-grade Agent orchestration | Open-sourced by Microsoft, supports plugins, workflows, memory, and multi-model collaboration |
| PydanticAI | Type-safe Agent development | Uses Python type system to constrain outputs, suitable for engineering applications |
| Mastra | Modern full-stack Agent framework | Supports Workflow, Memory, deployment, and observability capabilities |
| Agno (formerly Phidata) | Multi-Agent application framework | Emphasizes the combination capability of Agent + Knowledge + Tools |
| Camel-AI | Multi-Agent collaboration framework | Simulates team collaboration and task decomposition through role-playing mechanisms |
| MetaGPT | Software company simulation framework | Splits product, architecture, development, and testing into multiple roles for collaborative execution |
| Swarms | Agent cluster orchestration | Emphasizes large-scale Agent collaboration and task scheduling |
| Haystack Agent | Search and knowledge-augmented Agent | Suitable for enterprise search, document Q&A, and toolchain combinations |
| Atomic Agents | Composable Agent architecture | Emphasizes modular design and testability |
| DSPy | Declarative Prompt / Agent framework | Engineers and optimizes Prompt and reasoning processes |