How AI Agents work

AI agents simplify and automate user-directed tasks based on three main components:

1. Goal initialization and planning
2. Reasoning using available tools
3. Implementing tasks

Let’s look at each of these components more closely.

Goal initialization and planning

AI agents first require goals and environments designed by humans in order to act autonomously in their decision-making processes. A developer must first design the agentic system, then the developer must deploy and iterate based on agentic performance, and finally, the user will be able to provide the agent with specific goals to accomplish.

For example, a call center user might need to find specific information mentioned in a previous call. Assuming that all calls are recorded, transcribed, and stored (a critical first step for any application involving human speech), the user might ask the AI agent to “find the last time this customer discussed pricing during a call.” The AI agent could then pull relevant records from the call center’s database to find this information and summarize its findings back to the user. 

Note that this workflow makes it extremely important that the first step—transcribing the call—is of the highest accuracy in order to ensure the rest of the workflow can proceed accurately as well. 

This step-by-step process means that the AI agent can autonomously:

1. Operate on its own given natural language commands
2. Can pull relevant information
3. Can present this information back to the user in the most helpful way possible, whether that be a written summary, bullet points, visual representation, or a combination of both text and visuals

Reasoning using available tools

AI agents must determine which of their available tools—web searches, APIs, external datasets, etc.—they must access to fill inevitable knowledge gaps and pull the data required to tackle all of the necessary subtasks required to solve a problem.

Take the example of an AI agent that is designed to analyze customer support calls. Before it can attempt any analysis, the AI agent must first determine what information it needs to solve the task, such as a call transcript, a customer profile, or necessary product details. 

Implementing tasks

Finally, AI agents must methodically go through each of the tasks it thinks will achieve its goal based on the above two steps. This process usually involves the AI agent moving sequentially through each subtask, passing results and relevant information through the pipeline until the final answer is reached. 

Note that the AI agent may also create or act on additional subtasks as it evaluates if and when it has achieved its goal. 

If we continue with the customer support call example above, after the AI agent determines what information it needs, it then selects the tools it needs to fetch and process that data, such as speech-to-text API for transcription, a sentiment analysis model for customer satisfaction, or topic detection model for issue categorization. Finally, the AI agent synthesizes the results into human-readable actionable insights.

Want to learn more about building voice agents? Check out this YouTube video:

5 types of AI Agents

There are five main types of AI agents: simple reflex agents, model-based agents, goal-based agents, utility-based agents, and learning agents.

Here is a brief overview of each of these types:

• Simple reflex agents: The simplest type of agent, simple reflex agents function on a set of rules and do not hold any memory or interact with other agents to find missing information. If a simple reflex agent receives a prompt it isn’t prepared for, it simply can’t respond appropriately. 
• Model-based agents: Model-based reflex agents combine current perception plus memory to maintain an internal model of the world, updating this model as new information is received and processed. However, model-based agents are still limited by their own set of rules.
• Goal-based agents: Goal-based agents combine an internal model with a set of goals, searching for action sequences to reach their goals and planning these actions before actually taking them. This additional step makes them more effective than simple reflex or model-based agents. 
• Utility-based agents: Utility-based agents take this one step further by selecting a sequence of actions that reach their goal while also maximizing utility or reward by measuring the usefulness of an action based on a set of fixed criteria. 
• Learning agents: Learning agents may be utility or goal-oriented but are unique in their ability to learn, which occurs autonomously based on new experiences. 

Use cases

There are numerous varied use cases for AI agents, ranging from simple to complex.

Here is how a few industries are implementing AI agents: 

Customer service and support

Customer service and support applications are creating engaging, personalized experiences for customers by providing fast responses, personalizing recommendations, and offering conflict resolution with AI agents. The agents can also learn from interactions to provide recommendations that improve customer engagement and conversion. 

Content creation and marketing

AI agents are also being designed to automate and streamline content creation when integrated directly into a word processing program, offering recommendations for SEO optimization, generating new text, or adapting existing content for different audiences or markets. 

E-commerce and personalized recommendations

Similar to customer service applications, AI agents are being integrated into e-commerce engines to provide enhanced personalized shopping experiences for visitors, as well as to handle ongoing customer support needs. 

Healthcare and diagnostics

AI agents are being integrated into healthcare settings to automate administrative tasks like appointment scheduling, assist with questions about health conditions and treatment options, and provide proactive recommendations for interventions and preventative measures that can improve patient outcomes. 

Finance and investment

AI agents can also be used to manage personal finances, optimizing financial decisions and aligning investment strategies based on individual goals and risk levels, functioning as fill-in financial analysis and investment advisors. 

Education and training

Secondary and university-level education programs are integrating AI agents to analyze individual student performance and tailor instruction and pacing based on specific needs, customizing and improving the learning experience while boosting the reach of teachers with limited resources.