The video demonstrates how to build a code agent that can autonomously execute code based on instructions, showcasing its ability to solve mathematical problems like calculating the Fibonacci sequence and retrieving the current time using a time tool. It highlights the agent’s flexibility with various models and its potential applications in creative fields, such as 3D modeling in Blender, while promising more content on AI agents in the future.
In the second part of the “AI Agents for Beginners” series, the video focuses on building a code agent, which is an AI that can execute code based on given instructions. The code agent operates by utilizing a list of tools and generates a system prompt that describes these tools. This allows the agent to respond to queries by writing and executing code, while small agents are responsible for parsing the code, running it, and returning the results to the main model. To ensure safety, the agent is restricted from importing potentially harmful libraries, such as OS, which could lead to file deletion.
The video demonstrates the functionality of the code agent by posing a question about the 50th number in the Fibonacci sequence. The agent writes the necessary code to compute this value and successfully returns the correct answer. This showcases the agent’s ability to perform calculations and execute code autonomously, highlighting its practical applications in solving mathematical problems.
Next, the video introduces the implementation of a time tool that returns the current time based on a specified time zone. To integrate this tool with the small agents, the presenter wraps the existing code in a class called GetCurrentTimeTool. This class includes essential attributes such as name, description, inputs, and output type, ensuring that the model can utilize the tool effectively without making assumptions. The video then demonstrates the agent’s capability by asking for the current time in Boston, which it successfully retrieves.
The presenter emphasizes the flexibility of the code agent, noting that it can be used with various models, including those from Anthropic and OpenAI. For those looking to run agents for free, the Hugging Face inference API is recommended, while users with powerful GPUs can run quantized models locally. The video also mentions the possibility of using multiple Mac Minis for enhanced performance, suggesting that there are various options available depending on the user’s resources.
Finally, the video hints at the broader implications of AI agents, particularly in the context of MeshGen AI agents in Blender, which serve as a 3D modeling co-pilot. This example illustrates the potential of small agents in creative applications, and the presenter expresses excitement about the ongoing developments in the field of AI agents, particularly with the rising interest in Multi-Component Processing Systems (MCPS). The video concludes with a promise of more content on this topic in the future.