What Are AI Agents and What Do They Do

What Are AI Agents?

AI agents are autonomous intelligence systems that achieve goals and outcomes with minimal to no human intervention in their process towards those goals.   

These agents can make decisions, learn from data, and interact with their environment or users to achieve predefined goals. Unlike traditional rule-based automation, AI agents adapt to new data and improve their performance over time without constant human intervention. 

AI agents are more than just automated systems—they leverage machine learning, natural language processing (NLP), and other advanced AI technologies to make decisions in real time. They are designed to handle complex, repetitive tasks that would normally require human effort, allowing businesses to enhance productivity and free up valuable resources for more strategic activities. 

How Do AI Agents Work? 

AI agents have evolved from monolithic models—large, static systems designed for singular tasks—to compound AI systems that operate with greater adaptability and precision. These modern systems are composed of interconnected, specialized models that collaborate to deliver better results. 

A key feature of compound AI systems is their ability to access and integrate context data. For example, solutions like those provided by Skan AI analyze real-world workflows and processes, enabling AI agents to logically process the data they receive. This deeper understanding empowers the agents to make informed decisions, adapt to dynamic environments, and deliver more robust, actionable responses. 

By combining data-driven reasoning with contextual insights, compound AI systems offer unparalleled accuracy and efficiency, ensuring businesses get tailored, practical solutions to complex challenges. This shift underscores the power of AI to evolve and meet the demands of today’s fast-paced business environments.

What AI Agents Do: Use Cases

Let’s talk about use cases for AI agents. AI agents are incredibly versatile and can perform a wide range of tasks depending on the industry and use case. Here are some common examples of what AI agents can do:

1. How can AI Agents Help Customer Service and Support?

AI agents, particularly chatbots and virtual assistants, are increasingly used to improve customer service operations. They can interact with customers in real time, answering questions, resolving issues, and guiding users through processes. By automating common inquiries such as product recommendations, order tracking, and troubleshooting, AI agents offer a 24/7 support system that doesn’t require human intervention. One insurance giant leveraged their partnership with Skan AI to improve renewals and minimize complaints.

2. How can AI Agents Help Data Analysis and Insights?

AI agents can process large amounts of data far more quickly and accurately than humans. They are used to extract meaningful insights from raw data, identify trends, and make actionable recommendations. 

In financial services, for example, AI agents might analyze market data, predict potential risks, and suggest strategies to investment managers. With the right process intelligence, one info and analytics leader dramatically reduced turnaround time and improved subscription rates—all with a single tech partner. 

3. How can AI Agents Help Task Automation? 

A primary function of AI agents is automating routine tasks. This can range from simple actions like scheduling meetings to more complex processes such as managing supply chain logistics. By handling these tasks, AI agents reduce human error and increase operational efficiency, allowing employees to focus on more strategic endeavors.

4. How can AI Agents Help Insurance Underwriting and Claims Processing?

How are AI agents used in the insurance industry? They have a variety of applications. AI agents are increasingly used to streamline the underwriting and claims process.  

Underwriting typically involves assessing risk and determining appropriate coverage, a task that requires analyzing vast amounts of data, including customer information, historical claims, and external risk factors. AI agents can automate data analysis, evaluate risk profiles, and help underwriters make faster, more accurate decisions. 

And how are AI agents used in claims processing? Again, there’s numerous ways that AI agents can help with claims. AI agents can analyze the details of a claim and cross-reference them with policies, historical data, and fraud detection systems. AI can assess the validity of claims, detect inconsistencies or potential fraud, and even suggest optimal settlement amounts.  

By automating these processes, insurers can reduce processing time, minimize human error, and improve customer satisfaction. Tools like Skan AI, which maximizes use of agentic AI, help insurance companies save millions of dollars with process improvement.

5. How can AI Agents Help Predictive Maintenance?

Industries like manufacturing use AI agents to predict when equipment or machinery is likely to fail based on historical data and real-time monitoring. This predictive maintenance helps organizations reduce downtime, prevent costly repairs, and optimize resource allocation.

6. How can AI Agents Help Supply Chain and Logistics Optimization?

AI agents can help businesses optimize supply chain management by analyzing patterns, predicting demand, and automating inventory management. This enables companies to maintain optimal stock levels, reduce waste, and ensure timely delivery of goods.

What is The Difference Between AI Agents and AI Automation? 

AI agents are autonomous systems capable of learning, reasoning, and adapting to new data or environments, enabling them to make decisions independently. In contrast, AI automation focuses on executing predefined, rule-based tasks without flexibility or the ability to adapt to changes in real time.