AI Agents
Autonomous entities that perceive their environment and act to achieve goals using AI techniques.
Reactive Agents
Agents that act based on current perceptions without maintaining internal state or memory.
Deliberative Agents
Agents that plan ahead by using models of the world to make informed decisions.
Multi-Agent Systems
Systems where multiple agents interact, collaborate, or compete to achieve individual or collective goals.
Agent Architectures
Design patterns like layered, blackboard, or BDI used to structure AI agents.
Reinforcement Learning Agents
Agents that learn optimal behaviors by receiving rewards or penalties from the environment.
Prompt-based Agents
LLM-powered agents that use structured prompts to complete tasks autonomously.
Planning Agents
Agents that generate a sequence of actions to achieve specific objectives using search and planning algorithms.
Tool-Using Agents
Agents integrated with APIs, databases, or tools to perform complex tasks beyond natural language reasoning.
Agent Memory
The capability of an agent to store and recall information from past interactions to improve decision-making.
Agent Loop
The observe-think-act cycle that enables agents to continuously improve performance in dynamic environments.
Autonomous Agents
Agents capable of operating independently with minimal human intervention to complete tasks or missions.