Introduction to AI Agents and Agent Use Cases

Introduction

This lesson covers:

• What are AI Agents and what are the different types of agents?
• What use cases are best for AI Agents and how can they help us?
• What are some of the basic building blocks when designing Agentic Solutions?

Defining AI Agents and Types of AI Agents

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.

Let's break this definition into smaller parts:

System - It's important to think about agents not as just a single component but as a system of many components. At the basic level, the components of an AI Agent are:

• Environment - The defined space where the AI Agent is operating. For example, if we had a travel booking AI Agent, the environment could be the travel booking system that the AI Agent uses to complete tasks.
• Sensors - Environments have information and provide feedback. AI Agents use sensors to gather and interpret this information about the current state of the environment. In the Travel Booking Agent example, the travel booking system can provide information such as hotel availability or flight prices.
• Actuators - Once the AI Agent receives the current state of the environment, for the current task the agent determines what action to perform to change the environment. For the travel booking agent, it might be to book an available room for the user.

The different types of agents

Now that we have a general definition of AI Agents, let us look at some specific agent types and how they would be applied to a travel booking AI agent.

Agent Type

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.

When to Use AI Agents

In the earlier section, we used the Travel Agent use-case to explain how the different types of agents can be used in different scenarios of travel booking. We will continue to use this application throughout the course.

Let's look at the types of use cases that AI Agents are best used for:

• Open-Ended Problems - allowing the LLM to determine needed steps to complete a task because it can't always be hardcoded into a workflow.
• Multi-Step Processes - tasks that require a level of complexity in which the AI Agent needs to use tools or information over multiple turns instead of single shot retrieval.
• Improvement Over Time - tasks where the agent can improve over time by receiving feedback from either its environment or users in order to provide better utility.

Basics of Agentic Solutions

Agent Development

The first step in designing an AI Agent system is to define the tools, actions, and behaviors. In this course, we focus on using the Azure AI Agent Service to define our Agents. It offers features like:

• Selection of Open Models such as OpenAI, Mistral, and Llama
• Use of Licensed Data through providers such as Tripadvisor
• Use of standardized OpenAPI 3.0 tools

Best practices for using AI agents

AI agents have many benefits, but it's important to use them responsibly. Here are some best practices:

• Maintain control and compliance. Ensure that AI agents are governed by transparent and ethical guidelines. Compliance with regulations and industry standards is crucial to maintaining trust and integrity in AI processes.
• Ensure data privacy and security. When deploying AI agents, especially in sensitive environments like software development, it’s critical to make sure that the data used by these agents is handled securely. Implement encryption and access controls to protect both the data the AI agents process and the models themselves from unauthorized access or tampering. This is particularly important in scenarios involving customer data, proprietary code, or security-related tasks.
• Keep humans in the loop. Despite their autonomy, AI agents should operate under human supervision. Feedback from human users is vital for refining AI agents' performance and ensuring that they align with organizational goals.Regularly monitor agents’ performance to ensure they are functioning as expected. This includes evaluating the quality of their output, the relevance of their decisions, and their impact on the overall workflow. Continuous evaluation helps in identifying any drift in performance or unintended consequences, allowing for timely adjustments.
• Ensure transparency and explainability. It’s important to make sure that AI agents operate transparently, meaning that their decision-making processes should be understandable and explainable to humans. This helps in building trust with stakeholders and ensures that the AI agents’ actions align with the organization’s goals and ethical standards. Developing clear documentation and using explainable AI techniques can aid in making these processes more transparent.
• Focus on scalability and flexibility. As your organization’s needs evolve, so too should your AI agents. Implement AI solutions that are scalable and flexible, allowing them to adapt to changing demands and integrate with new tools or workflows. This ensures that your AI agents can continue to provide value as your development processes grow in complexity.
• Keep ethical considerations at the forefront. Ethics play a critical role in the deployment of AI agents. Ensure that your AI systems are designed and used in ways that respect human rights, avoid bias, and contribute positively to society. Establish ethical guidelines for the development and deployment of AI agents and regularly review these guidelines to ensure they remain relevant as technology advances.