Psychologist Daniel Kahneman introduced two types of thinking: System 1 and System 2. These are two different ways our minds process information.
System 1 and System 2 Thinking in Agentic AI Applications
• System 1 thinking is quick, intuitive, and automatic. It relies on patterns and past experiences, making it ideal for tasks that are routine or where fast responses are needed.
• System 2 thinking is slower, more deliberate, and analytical. This is used for handling complex or unfamiliar situations where deep reasoning is required.
When it comes to Agentic AI applications, their main job is to break down complex problems into smaller tasks, plan how to tackle them, and then execute the tasks to come up with a solution.
For instance, an AI agent might need to:
1. Understand the problem,
2. Break it down into smaller tasks,
3. Plan out steps for each task,
4. Execute those steps,
5. Combine all the results to come up with a final solution.
It seems natural to use the slower, more thoughtful System 2 thinking for understanding the problem and planning, while the faster System 1 thinking would be useful for carrying out tasks and pulling the results together.
After learning more about OpenAI’s o1 model, I’m wondering if it could handle the more deliberate System 2 thinking, such as problem understanding and task planning. Meanwhile, other models like OpenAI GPT-4o, Google Gemini Pro, Meta Llama, or Anthropic Claude, which don’t use “Chain of Thought” reasoning during inference, could be used for the faster, more intuitive System 1 thinking. While these models can still do some reasoning, they might not be as effective at breaking down complex problems and planning tasks as the o1 model, unless the problems are simple and somewhat known.
Examples of Agentic AI Application
For example, an Agentic AI application might research the best stocks to invest in by analyzing data and using predefined rules. Models like GPT-4o, Gemini Pro, Llama, or Claude could handle this well. But if you have a specific budget and want to maximize returns over a two-year period, a model like o1 might be better suited because it can engage in deeper, more deliberate thinking to achieve the best outcome.
By combining both types of thinking, Agentic AI applications can respond quickly to routine tasks while reserving deeper analysis for more complex problems. This hybrid approach can make systems more flexible, cost-effective, and capable of tackling both simple and intricate challenges.
I’d love to hear your thoughts on this approach as we continue to explore how different AI models can be used to solve complex problems for our enterprise customers.