Hugging Face recently released the first unit of their AI Agents course. I wanted to share some takeaways about how AI agents function.
What is an AI Agent?
An AI Agent is essentially a system that combines an AI model (typically an LLM) with the ability to interact with its environment. Think of it like a digital assistant that can understand natural language requests, plan and reason about how to fulfill those requests, take actions using tools to accomplish tasks, and learn from the results of those actions.
The Three Core Components
The course introduced three fundamental components that make up an agent’s workflow:
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Thoughts: The agent’s internal reasoning process where it analyzes the current situation, plans the next steps, decides which actions to take, and uses the ReAct approach (Reasoning + Acting) for step-by-step planning.
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Actions: How the agent interacts with its environment through tools (functions or APIs it can call), structured formats (usually JSON or code), and the “stop and parse” approach for reliable execution.
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Observations: How the agent processes feedback by collecting results from actions, updating its understanding, and adapting its strategy based on outcomes.
The Role of LLMs
Large Language Models serve as the “brain” of AI agents:
- LLMs work by predicting the next token in a sequence
- They use special tokens to structure their input and output
- Messages are formatted using chat templates specific to each model
- The system prompt defines the agent’s behavior and available tools
Practical Implementation
Key insights of building a simple agent using smolagents:
- How to define tools using Python decorators
- The importance of well-structured documentation for tools
- How to use the system prompt to give an agent access to tools
- The iterative nature of the agent’s decision-making process
Real-World Applications
The course demonstrated several practical applications:
- Personal virtual assistants
- Customer service chatbots
- AI NPCs in video games
- Task automation systems
Conclusion
This new Hugging Face course demystifies AI agents. Rather than being magical black boxes, they’re structured systems that combine LLMs for reasoning, tools for taking action, and a clear workflow for processing and responding.
If you’re interested in AI agents, I highly recommend checking out the course yourself, or even the source code on GitHub.