In customer support, AI agents act as intelligent assistants, answering questions, guiding users through common issues, and even performing simple actions automatically. For example, when a customer asks, "Where is my order?" or "How do I return this product?", an AI agent can quickly check company systems, retrieve relevant information such as shipping status or return policies, and generate a clear, personalized response without human intervention.

AI agents can also assist employees in finding information within large organizations. Companies often store thousands of documents, including meeting notes, policy documents, presentations, and legal materials. Manually searching through these is time-consuming and inefficient. AI agents streamline this process by allowing staff to ask questions in natural language, e.g., "What were the major changes to the company policy last year?" The agent retrieves the most relevant documents and provides a concise, understandable summary, often with links to the original sources.

In practice, these two functions can be combined into a single system. A large international e-commerce company could deploy an AI agent that supports both external customers and internal staff. Customers use it to track orders or initiate returns, while employees access internal knowledge using the same system. This reduces employee workload and speeds up response times.

1. Sapkota, Ranjan, Konstantinos I. Roumeliotis, and Manoj Karkee. “AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges.” Information Fusion 126 (February 2026): 103599.

In healthcare, particularly in critical areas such as ICU units, advanced AI, known as Agentic AI, is being explored to support complex medical decision-making. Unlike single-purpose AI agents, Agentic AI systems consist of multiple specialized AI agents that collaborate, each covering a specific aspect of patient care.

Each agent assumes different roles: one monitors vital signs and lab results to detect early warnings like infection risk or organ failure; another retrieves and summarizes the patient's medical history; a third compares the current situation with established medical guidelines and suggests potential treatment options.

Agents share information and coordinate their actions to produce coherent and medically sound recommendations. A coordinating component flags conflicting information, while final decisions remain under human physician oversight. Feedback from doctors can be stored and used to improve future performance. Early studies and pilot projects suggest Agentic AI could significantly support medical staff.

1. Sapkota, Ranjan, Konstantinos I. Roumeliotis, and Manoj Karkee. “AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges.” Information Fusion 126 (February 2026): 103599. https://doi.org/10.1016/j.inffus.2025.103599

In robotics and automation, Agentic AI coordinates multiple robots to collaborate effectively in complex real-world environments. Instead of programming each robot with fixed instructions, Agentic AI allows each robot to act as a specialized agent, while a central coordination system monitors operations and adjusts plans when conditions change.

An example in agriculture: in a modern apple orchard, multiple robots and drones work together to optimize harvesting. Drones map the orchard, estimate fruit yield, and identify ripe fruits. This data is shared with all robot agents. Harvesters are assigned to areas with highest yield, transport robots move harvested fruits, and navigation agents continuously adjust routes to avoid obstacles or humans. If weather changes or a robot fails, tasks are automatically reassigned to maintain operations.

1. Sapkota, Ranjan, Konstantinos I. Roumeliotis, and Manoj Karkee. “AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges.” Information Fusion 126 (February 2026): 103599. https://doi.org/10.1016/j.inffus.2025.103599