AI agents are a type of compound AI system that uses a large language model (LLM) to control its logic. Compound AI systems combine models and other components to solve problems, and they are more adaptable and effective than models alone.
Here are some key points about AI agents from the sources:
- Compound AI Systems: These systems combine different components, including models, programmatic elements, and tools, to solve complex tasks. They are modular and can be adapted more quickly than monolithic models.
- Control Logic: The path that a program takes to answer a query is known as its control logic. This can be defined programmatically or by an LLM.
- Agentic Approach: This approach uses an LLM to manage the logic of a system. The LLM is prompted to create a plan, reason through each step, and use tools to solve problems.
- Reasoning and Acting: LLM agents can reason through complex problems and use external tools to act on their plans. Tools can be search engines, calculators, other language models, or any external program.
- Memory: Agents can access and use memory to store conversation history, internal logs of thinking, and other data.
- ReACT: This is a popular method of configuring agents that combines reasoning and acting. The agent will plan, act using tools, observe results, and iterate on the plan as needed.
- Sliding Scale of Autonomy: AI systems can be configured with different degrees of autonomy. For narrow, well-defined problems, a programmatic approach may be more efficient, while for complex tasks, an agentic approach can be helpful.
The sources suggest that AI agents are a rapidly developing technology that combines system design and agentic behavior.
https://youtu.be/F8NKVhkZZWI?si=q0dVZ1juEjPLi5s3
