An AI agent is an integrated system that extends beyond basic text generation by augmenting Large Language Models (LLMs) with tools and domain knowledge.
Here’s a closer look at the underlying elements:
System: Each AI agent functions as an interconnected ecosystem of components. Below is a list of key factors and components that affect system performance:
Large Language Models: While agents have long existed, LLMs enhance these systems with powerful language comprehension and data-processing capabilities.
Action Execution: Rather than just produce text, LLMs within an agent context interpret user instructions and interact with tools to achieve specific objectives.
Tools: The agent’s available toolkit depends on the software environment and developer-defined boundaries. In the travel agent example in this Learning Path, these tools might be limited to flight and hotel reservation APIs.
Knowledge: Beyond immediate data sources, the agent can fetch additional details - perhaps from databases or web services - for enhanced decision-making.
AI agents come in multiple forms. The table below provides an overview of some agent types and examples of their roles in a travel booking system:
| Agent Category | Key Characteristics | Example Usage in a Travel Booking System |
|---|---|---|
| Simple Reflex Agents | Act directly based on predefined rules or conditions. | Filters incoming messages and forwards travel-related emails to a service center. |
| Model-Based Agents | Maintain an internal representation of the world and update it based on new inputs. | Monitors flight prices and flags dramatic fluctuations, guided by historical data. |
| Goal-Based Agents | Execute actions with the aim of meeting designated objectives. | Determines the necessary route (flights, transfers) to get from your current location to your target destination. |
| Utility-Based Agents | Use scoring or numerical metrics to compare and select actions that fulfill a goal. | Balances cost versus convenience when determining which flights or hotels to book. |
| Learning Agents | Adapt over time by integrating lessons from previous feedback or experiences. | Adjusts future booking suggestions based on traveler satisfaction surveys. |
| Hierarchical Agents | Split tasks into sub-tasks and delegate smaller pieces of work to subordinate agents. | Cancels a trip by breaking down the process into individual steps, such as canceling a flight, a hotel, and a car rental. |
| Multi-Agent Systems | Involve multiple agents that might cooperate or compete to complete tasks. | Cooperative: Different agents each manage flights, accommodations, and excursions. Competitive: Several agents vie for limited rooms. |
AI agents come in multiple forms. They can be grouped into various categories and excel in a wide range of applications: