Building Smart AI Agents: A Step-by-Step Guide

Posted June 27, 2024

Ever been torn about making a purchase that seemed cool but also a bit questionable? You might find yourself overthinking, just like those moments before buying a pair of Crocs with plugs that make them look less like Swiss cheese.

Now, imagine if we could delegate complex decision-making to AI agents. In this post, you'll learn how to create a strong team of AI agents to tackle challenging problems.

Understanding System 1 and System 2 Thinking

Daniel Kahneman, in his book "Thinking, Fast and Slow," discusses two types of thinking: System 1 and System 2. System 1 is fast and automatic, like recognizing a face in a crowd. System 2 is slow, deliberate, and logical, requiring effort and time.

AI, as it stands today, mainly operates with System 1. It can quickly generate responses but lacks the depth to solve complex problems requiring thorough thought. However, strides are being made to bridge this gap.

Methods to Simulate System 2 Thinking in AI

Tree of Thought Prompting

One way to simulate rational thinking in AI is through tree-of-thought prompting. This approach makes the AI consider an issue from multiple perspectives before arriving at a decision, much like a team of experts discussing a problem.

Building a Team with CreAI and Agent Systems

Another method is using platforms like CreAI. This tool allows anyone, even non-programmers, to build custom agents that can work together. By leveraging APIs or local models, you can assemble a smart AI team. Here’s how you can get started.

Step-by-Step Guide to Building AI Agents

Setting Up the Environment

1. Open VS Code and start a new terminal.
2. Create and activate a virtual environment.
3. Install CreAI by typing the appropriate command in the terminal.
4. Import necessary modules and set your OpenAI API key.

Defining Your Agents

Each agent should have a specific role and clearly defined goals. For example:

Assigning Tasks

Tasks should be specific and have clear results. Define tasks for each agent.

Running the Process

Define how the agents will work together—usually in a sequential manner where the output of one agent becomes the input for the next. Then run the process.

Enhancing Agent Intelligence

Adding Real-World Data Tools

You can make agents smarter by giving them access to real-world data. Two options are:

1. Built-in Tools in LangChain: Tools like 11Labs text-to-speech, Google data access, and Wikipedia.
2. Custom Tools: Create tools that provide agents with specific data needs, like scraping Reddit posts.

Using Local Models

To avoid paying for API calls and keep data private, run local models. These require significant RAM, so ensure your system specifications can handle it.

Experimenting with Models

Through extensive testing, it's found that certain models perform better than others. For example, OpenChat produced a decent newsletter resembling a human writer's work, while models like Mistral struggled. Keep testing to find the best fit for your needs.

Conclusion

By building a team of AI agents, you can automate complex tasks and make smarter decisions without the usual overthinking. Whether using tree-of-thought prompting, CreAI, or custom tools, the future of AI looks promising in achieving System 2-like rationality. So, dive in and start building your team of smart AI agents today.