To fully appreciate AI agent architectures, it's essential to understand their core components. Each component plays a unique role in how an AI agent functions and interacts with its environment.
The profiling module helps define an AI agent’s role and function within a specific context. Think of it as the agent's identity card.
- Creating Agent Profiles: There are various ways to create these profiles. Handcrafting involves manually defining the agent’s characteristics. LLM-generation uses large language models to generate profiles, while dataset alignment aligns the agent's profile with specific datasets.
- Influence on Other Modules: The profile directly affects other modules like memory and planning, shaping how the agent stores information and strategizes.
Memory is crucial for AI agents to store and recall past behaviors. Without memory, an agent would be like a goldfish, forgetting everything in the blink of an eye.
- Types of Memory: AI agents use short-term memory for immediate tasks and long-term memory for storing learned experiences.
- Memory Formats: Information can be stored in various formats, such as natural languages, embeddings, and databases, depending on the agent’s needs.
The planning module is where the magic of strategy happens. This module enables AI agents to think ahead and plan for complex tasks.
- Planning Without Feedback: This involves breaking down tasks into subgoals and considering multiple paths to achieve these goals.
- Planning With Feedback: Here, the agent adjusts its plans based on feedback from the environment, humans, or its own models.
The action module is the muscle of the AI agent, translating decisions into specific actions.
- Action Targets: These can vary widely, from completing tasks and engaging in dialogues to exploring environments.
- Action Strategies: Agents might use memory recollection, engage in multi-round interactions, or adjust actions based on feedback to achieve their goals. Understanding these components helps you grasp how AI agents operate and interact with their surroundings. Each module plays a specific role, contributing to the agent’s overall functionality and effectiveness.