- What are AI Agents?
- AI Agents are systems that enable Large Language Models(LLMs) to perform actions by extending their capabilities by giving LLMs access to tools and knowledge.
- The different types of agents
Agent Type Description Example Simple Reflex Agents Perform immediate actions based on predefined rules. Travel agent interprets the context of the email and forwards travel complaints to customer service. Model-Based Reflex Agents Perform actions based on a model of the world and changes to that model. Travel agent prioritizes routes with significant price changes based on access to historical pricing data. Goal-Based Agents Create plans to achieve specific goals by interpreting the goal and determining actions to reach it. Travel agent books a journey by determining necessary travel arrangements (car, public transit, flights) from the current location to the destination. Utility-Based Agents Consider preferences and weigh tradeoffs numerically to determine how to achieve goals. Travel agent maximizes utility by weighing convenience vs. cost when booking travel. Learning Agents Improve over time by responding to feedback and adjusting actions accordingly. Travel agent improves by using customer feedback from post-trip surveys to make adjustments to future bookings. Hierarchical Agents Feature multiple agents in a tiered system, with higher-level agents breaking tasks into subtasks for lower-level agents to complete. Travel agent cancels a trip by dividing the task into subtasks (for example, canceling specific bookings) and having lower-level agents complete them, reporting back to the higher-level agent. Multi-Agent Systems (MAS) Agents complete tasks independently, either cooperatively or competitively. Cooperative: Multiple agents book specific travel services such as hotels, flights, and entertainment. Competitive: Multiple agents manage and compete over a shared hotel booking calendar to book customers into the hotel.