Build an AI Agent

About 5 min

👑 Part 1: The Core Concepts

This is the essential mindset. Once you understand this part, you'll grasp what an AI Agent truly is and how it fundamentally differs from a standard program.

An AI Agent is a software entity equipped with autonomy, perception, and the ability to act. Unlike a traditional program that simply "gets called and returns a value," an agent can decide for itself "what to do next" based on a high-level goal.

Difference from a standard program: If you give a calculator 2+2, it must return 4. If you give an agent a goal like "Book a flight to Shanghai for me for next week," it will autonomously browse for flights, compare prices, maybe even ask for your preferences, and then complete the booking.
Core Characteristics: Autonomy, Goal-Driven, Reactivity, Social Ability.

2. ReAct: The Agent's Fundamental Thinking Pattern

This is the core operational loop for the vast majority of current agents, originating from Google's famous paper, ReAct (Reason + Act). Understanding this is like grasping the "soul" of an agent.

The ReAct Loop: Observation -> Thought -> Action

Observation: The agent perceives the current state. For example, the user's command is "What's the weather like in Beijing today?", or the result of the previous action is "Webpage opened successfully, the content is...".
Thought: The agent reasons based on the observed information. This is the most critical step, driven by a Large Language Model (LLM).
- It thinks: "The user's goal is to check the weather. I need a tool that can do this. I should call the 'weather_query' tool with the parameter 'Beijing'."
Action: The agent executes the decision made during the thought process.
- This action could be calling a tool (e.g., search_weather(city="Beijing")) or responding to the user (e.g., "It's sunny in Beijing today, with a temperature range of 15-25°C.").
Repeat the Loop: The action produces a new observation (e.g., the tool returns weather data). The agent enters the thought phase again, continuing this cycle until the final goal is achieved.

🛠️ Part 2: The Tech Stack Breakdown

These are the "organs" of an agent; each part is critically important.

1. The Brain: Large Language Models (LLMs)

The LLM is the reasoning core of the agent, responsible for the "Thought" step.

How to Use:
- API Calls: The mainstream method. You'll need to register for an API Key from platforms like OpenAI (GPT-4/GPT-3.5), Anthropic (Claude 3), or 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 (e.g., Llama 3, Qwen, Mistral) on your own machine.
Core Skill: Prompt Engineering
- This is the only way you communicate with the LLM and is a core skill for any agent developer.
- Role-playing: You are a helpful assistant. This makes the LLM adopt a specific persona.
- Chain-of-Thought (CoT): Let's think step by step. This guides the LLM to output its detailed reasoning process, significantly improving accuracy on complex tasks.
- Providing Tool Information: You must clearly describe in the prompt what tools the agent has available, including their descriptions and parameters.

2. Memory: Short-term & Long-term

An agent without memory has the memory of a "goldfish" and cannot perform complex tasks.

Short-term Memory:
- Implementation: Typically a chat history buffer (a queue of recent messages).
- Purpose: To remember the immediate context and allow for a coherent conversation. For instance, if you first ask, "Check the weather in Beijing," and then say, "What about Shanghai?", the agent knows you're still asking about the weather.
Long-term Memory:
- Core Technology: RAG (Retrieval-Augmented Generation).
- Implementation Flow:
  1. Data Processing: Take your private knowledge (like PDFs, Word docs, TXT files) and split it into smaller chunks.
  2. Embedding: Convert each text chunk into a string of numbers (a vector) using an Embedding model (e.g., OpenAI's text-embedding-3-small). This vector represents the semantic meaning of the text.
  3. Store in a Vector Database: Store the text chunks and their corresponding vectors in a Vector Database.
  4. Retrieve: When a user asks a question, convert the question into a vector as well. Then, search the database for the most semantically similar text chunks.
  5. Generate: Send the retrieved text chunks to the LLM as context, along with the user's original question, and ask it to generate an answer based on this information.
- Common Tools:
  - Vector Databases: ChromaDB (local, simple), Pinecone (cloud-based, managed), Weaviate, FAISS (Facebook's open-source library).

3. Tools: Connecting to the Physical and Digital Worlds

Tools are the concrete implementation of an agent's "Actions," allowing it to go beyond simple text-based conversation.

Common Tool Types:
- Web Search: Calling the Google/Bing/DuckDuckGo API.
- Code Execution: Providing a secure Python interpreter environment for calculations, data analysis, or plotting.
- API Calls: Connecting to any service with an API (weather, stocks, calendars, internal company systems).
- File I/O: Reading from and writing to local files.
How It Works: In its "Thought" step, the LLM decides which tool to use and generates the required parameters in a JSON format. 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, low-level work for you.

1. LangChain 🦜🔗

Positioning: "The All-in-One Swiss Army Knife." It is currently the most popular and feature-rich agent framework.
Core Modules:
- Chains: Link together steps like LLM calls, tool usage, and data preprocessing into a logical chain.
- Agents: Provides built-in agent types (e.g., ReAct Agent, Plan-and-Execute Agent). You just need to define the LLM and tools, and it will run autonomously.
- Memory: Offers various plug-and-play memory modules.
- Tool Integrations: Comes with a huge library of pre-built third-party tool integrations.
Pros: Massive ecosystem, comprehensive features, active community.
Cons: Can be overly abstracted, making debugging difficult at times; has a steeper learning curve.

2. LlamaIndex 🦙

Positioning: "The Data Specialist." It focuses on RAG, helping you easily build agents that can converse with your own data.
Core Features:
- Powerful Data Loaders.
- Optimized data indexing and retrieval strategies.
- Seamless integration with frameworks like LangChain.
When to Choose: When your core requirement is building a Q&A or analysis system around a large volume of private documents, LlamaIndex is the top choice.

3. AutoGen 🤖🤝🤖

Positioning: "The Multi-Agent Collaboration Platform." Open-sourced by Microsoft, it's used for building complex systems where multiple agents work together.
Core Concept: You can define agents with different roles and capabilities (e.g., "Product Manager," "Programmer," "QA Engineer") and have them collaborate in a chat environment to complete a complex task (e.g., "develop a Snake game").
When to Choose: When a single agent struggles to complete a task and you need a division of labor among different roles.

How to Choose?

Beginner/General Purpose: Start with LangChain.
Heavy Reliance on Private Data/RAG: Prioritize LlamaIndex.
Exploring Complex Tasks/Multi-Role Collaboration: Try AutoGen.

🗺️ Part 4: The Practical Path (From Zero to Hero)

Follow this project path, and your skills will grow exponentially.

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

Goal: Build a bot that can search the internet to 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: Create an agent that can read your PDF documents and answer related questions.
Tech Stack: LlamaIndex / LangChain + Embedding Model + ChromaDB.
Learning Points:
- Master the full RAG pipeline: Load -> Chunk -> Embed -> Index -> Retrieve.
- Learn how to use a vector database.
- Understand how context helps LLMs reduce hallucinations.

Level 3: AI Research Assistant

Goal: Give the agent a research topic, and have it automatically browse the web, read sources, summarize findings, and generate a research report.
Tech Stack: LangChain + multiple tools (web search, file reading, content summarization).
Learning Points:
- How an agent can 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 to complete a simple software requirement.
Tech Stack: AutoGen.
Learning Points:
- Understand the patterns of multi-agent collaboration.
- How to design agents with different roles and enable effective communication between them.

📚 Part 5: Advanced Topics & Learning Resources

Advanced Topics

Agent Evaluation: How do you measure if your agent is performing well? This is a major industry challenge. Check out tools like LangSmith and AgentOps.
Planning & Task Decomposition: For highly complex tasks, the agent needs to create a plan first and then execute it step-by-step.
Model Fine-tuning: When general-purpose models don't meet the needs of a specific domain, you'll need to fine-tune a model on your own data.
Safety & Reliability: How to prevent an agent from executing dangerous operations (like deleting files) or from being compromised by malicious prompts.

Learning Resources

Must-Read Papers: Google's ReAct paper, the Self-Ask paper.
Official Documentation: The official docs for LangChain, LlamaIndex, and AutoGen are your best learning materials.
Industry Blogs: Lilian Weng's blog (lilianweng.github.io/posts/), especially her post on LLM-powered agents.
Online Courses: The free short courses on LangChain and Prompt Engineering by Andrew Ng on deeplearning.ai.
Communities: Join the Discord and GitHub communities for these projects to follow discussions and the latest developments.