From the course: Transforming Business with AI Agents: Autonomous Efficiency and Decision-Making

Utility-based agents

- A utility-based agent focuses on maximizing the value or utility of a specific goal. The utility can be anything, from maximizing profits to minimizing energy consumption. The utility-based AI agent not only predicts an outcome, but also calculates the potential utility of every outcome to determine the optimal actions it needs to take to maximize value for a particular situation. Unlike goal-based agents, utility-based agents don't have a specific goal. Instead, humans design them to find the best solution for a specific utility by using a predetermined set of criteria, which represent the goals or objectives of the AI agent. These utilities function also to evaluate how well different actions achieve the different goals. Utility-based agents excel at prioritizing tasks and allocating resources or budgets leading to greater efficiency and better resource utilization. Some examples of utility-based agents include managing an investment portfolio, allocating a marketing budget, project prioritization or inventory management. Utility-based agents can also optimize resources like time, such as prioritizing work schedules to maximize value by taking into account vacation requests, work requirements, and available skills. They can also help with selecting product features or vendors to optimize against a set of criteria based on what different customers are willing to pay. And utility-based agents can also learn from their environment, resulting in more reliable results. Given their flexibility, utility-based agents also excel in dynamic environments where they can adjust their decision depending on new information or changing circumstances. Similarly, they can tailor the decision-making and strategies to reflect unique preferences or specific constraints, resulting in customization and personalization that maximizes value. But if you routinely optimize and prioritize resources, schedules, or portfolios, be confident that you can define and measure value and have reliable data input sources leveraging utility-based agents. Like other agents, there are some significant limitations to utility-based agents, namely that they depend on an accurate definition of value. If they don't accurately capture the objectives or goals, the AI agent will deliver suboptimal results. These agents work best when goals can be clearly quantified and measured. These agents also depend on complete and accurate information to conduct its value calculations. For example, for a work schedule optimization agent, it needs to collect complete shift requests from all workers. If even one person's information is missing, it can't optimize the work schedule. Finally, developing and fine-tuning the value calculations and decision-making models can be complex, time-consuming and consume a lot of compute time. Now let's look at learning-based agents, which address many of these issues.

Contents