Skip to main content

Steps to Develop an AI Agent

[email protected]6/14/25...About 5 minAI Knowledge

👑 Part 1: Core Concepts (The Core Concepts)

This is the essence. Once you understand this part, you'll know what an AI agent is and how it fundamentally differs from ordinary programs.

1. What is an AI Agent?

An AI agent is a software entity with autonomy, perception, and action capabilities. Unlike traditional programs that simply "call and return," an agent can decide "what to do next" based on a broad goal.

  • Difference from ordinary programs: If you give a computer 2+2, it must return 4. If you give an agent a goal like "help me book a flight to Shanghai next week," it will search for flights, compare prices, even ask for your preferences, and then complete the booking.
  • Core features: Autonomy, goal-driven, reactivity, social ability.

2. ReAct: The Foundational Thinking Pattern of Agents

This is the core working loop of most agents today, from Google's famous ReAct (Reason + Act) paper. Understanding it is like grasping the "soul" of agents.

ReAct Loop: Observation -> Thought -> Action

  1. Observation: The agent perceives the current state. For example, the user's instruction is "What's the weather in Beijing today?" or the result of the last action is "Webpage opened successfully, content is...".
  2. Thought: The agent reasons based on the observed information. This is the most critical step, powered by a large language model (LLM).
    • It might think: "The user's goal is to check the weather. I need a tool for weather lookup. I should call 'search_weather(city="Beijing")'."
  3. Action: The agent executes the decision made in the thought step.
    • This action could be calling a tool (e.g., search_weather(city="Beijing")) or replying to the user (e.g., "Beijing is sunny today, 15-25°C.").
  4. Repeat the loop: The action produces new observations (e.g., the tool returns weather data), and the agent enters the thought phase again, until the final goal is achieved.

🛠️ Part 2: Key Technologies (The Tech Stack)

This is the "engine room" of agents, and every part is crucial.

1. The Brain: Large Language Models (LLMs)

LLMs are the reasoning core of agents, responsible for the "thought" phase.

  • How to use:
    • API calls: The mainstream approach. You need to register for an API key from platforms like OpenAI (GPT-4/GPT-3.5), Anthropic (Claude 3), Google (Gemini).
    • Local deployment: If privacy or cost is a concern, you can use tools like Ollama or LM Studio to run open-source models (Llama 3, Qwen, Mistral) locally.
  • Core skill: Prompt Engineering
    • This is your only way to communicate with the LLM and is the core skill for agent developers.
    • Role-playing: You are a helpful assistant. Let the LLM assume a specific role.
    • Chain-of-Thought (CoT): Let's think step by step. Guide the LLM to output detailed reasoning, greatly improving accuracy on complex tasks.
    • Provide tool information: You must clearly tell the LLM in the prompt which tools are available and their descriptions/parameters.

2. Memory: Short-term and Long-term Memory

An agent without memory is a "goldfish" and cannot perform complex tasks.

  • Short-term memory:
    • Implementation: Usually a chat history buffer.
    • Function: Remembers context, allowing conversations to continue. For example, if you first ask "Check the weather in Beijing," then "What about Shanghai?", the agent knows you're still asking about the weather.
  • Long-term memory:
    • Core technology: RAG (Retrieval-Augmented Generation).
    • Workflow:
      1. Data processing: Chunk your private knowledge (PDF, Word, TXT docs).
      2. Embedding: Convert each chunk into a vector using an embedding model (e.g., OpenAI text-embedding-3-small), representing the semantic meaning.
      3. Store in vector database: Store the chunks and their vectors in a vector database.
      4. Retrieval: When the user asks a question, convert it to a vector and search for the most similar chunks in the database.
      5. Generation: Provide the retrieved chunks as context to the LLM, so it can answer based on this information.
    • Common tools:
      • Vector databases: ChromaDB (local, simple), Pinecone (cloud, managed), Weaviate, FAISS (Facebook open source).

3. Tools: Connecting the Physical and Digital Worlds

Tools are the concrete implementation of the agent's "actions," allowing it to go beyond pure text interaction.

  • Common tool types:
    • Web search: Call Google/Bing/DuckDuckGo APIs.
    • Code execution: Provide a safe Python interpreter for calculations, data analysis, plotting.
    • API calls: Connect to any service with an API (weather, stocks, calendar, internal company systems).
    • File operations: Read/write local files.
  • Working principle: The LLM decides which tool to use in the "thought" step and generates the required JSON parameters. Your code is responsible for parsing this JSON and executing the corresponding function.

🚀 Part 3: Mainstream Development Frameworks

Building from scratch is cool, but inefficient. Frameworks handle a lot of the tedious groundwork for you.

1. LangChain 🦜🔗

  • Positioning: "The Swiss Army knife." Currently the most popular and feature-rich agent framework.
  • Core modules:
    • Chains: String together LLM calls, tool usage, data preprocessing, etc., into a logical chain.
    • Agents: Built-in agent types (e.g., ReAct Agent, Plan-and-Execute Agent). You just need to define the LLM and tools, and it runs automatically.
    • Memory: Provides various plug-and-play memory modules.
    • Tool Integrations: Wraps a huge number of third-party tools, ready to use.
  • Pros: Huge ecosystem, comprehensive features, active community.
  • Cons: Sometimes too abstract, can be hard to debug, steeper learning curve.

2. LlamaIndex 🦙

  • Positioning: "Data processing specialist." Focuses on RAG, helping you easily build agent systems that converse with your own data.
  • Core features:
    • Powerful data loaders.
    • Optimized indexing and retrieval strategies.
    • Seamless integration with LangChain and other frameworks.
  • When to choose: If your core need is building Q&A or analysis systems around large volumes of private documents, LlamaIndex is a top choice.

3. AutoGen 🤖🤝🤖

  • Positioning: "Multi-agent collaboration platform." Open-sourced by Microsoft for building complex systems with multiple collaborating agents.
  • Core concept: You can define agents with different roles and abilities (e.g., "product manager," "developer," "tester") and have them work together in a chat group to complete a complex task (e.g., "develop a snake game").
  • When to choose: When a single agent can't complete the task and you need different roles to collaborate.

How to choose?

  • Beginner/general purpose: Start with LangChain.
  • Heavy reliance on private data/RAG: Prefer LlamaIndex.
  • Exploring complex/multi-agent collaboration: Try AutoGen.

🗺️ Part 4: Practical Roadmap (From Zero to Hero)

Follow this path and your skills will spiral upward.

Level 1: Basic Q&A Bot (Hello, Agent!)

  • Goal: Build a bot that can search the web and answer questions.
  • Tech stack: LangChain + OpenAI API + Google Search API.
  • Learning points:
    • Understand and implement a simple ReAct loop.
    • Learn how to define and use a tool.
    • Master basic prompt engineering.

Level 2: Personal Knowledge Base Assistant

  • Goal: Let the agent read your PDF documents and answer related questions.
  • Tech stack: LlamaIndex / LangChain + Embedding Model + ChromaDB.
  • Learning points:
    • Master the full RAG workflow: loading -> chunking -> embedding -> indexing -> retrieval.
    • Learn to use vector databases.
    • Understand how context helps LLMs reduce hallucination.

Level 3: AI Research Assistant

  • Goal: Give the agent a research topic, let it search online, read, summarize, and generate a research report.
  • Tech stack: LangChain + multiple tools (web search, file reading, summarization).
  • Learning points:
    • How agents autonomously plan and use multiple tools.
    • More complex task decomposition and state management.

Level 4: Automated Software Development Team

  • Goal: Use AutoGen to simulate a development team and complete a simple software requirement.
  • Tech stack: AutoGen.
  • Learning points:
    • Understand multi-agent collaboration models.
    • How to design different agent roles and enable effective communication.

📚 Part 5: Advanced Topics and Learning Resources

Advanced Topics

  • Agent Evaluation: How to measure your agent's performance? This is a tough industry problem. Check out LangSmith, AgentOps, etc.
  • Planning and Task Decomposition: For very complex tasks, agents need to plan first, then execute step by step.
  • Model Fine-tuning: When general models can't meet specific needs, you need to fine-tune on your own data.
  • Safety & Reliability: How to prevent agents from performing dangerous operations (like deleting files) or being attacked by malicious prompts.

Learning Resources

  • Must-read papers: Google's ReAct paper, Self-Ask paper.
  • Official docs: LangChain, LlamaIndex, AutoGen official docs are the best learning materials.
  • Industry blogs: Lilian Weng's blog (lilianweng.github.io/posts/), especially articles on LLM-powered agents.
  • Online courses: Andrew Ng's free short courses on LangChain and Prompt Engineering at deeplearning.ai.
  • Community: Join relevant Discord and GitHub projects, follow discussions and the latest progress.