An AI agent is a system that goes beyond answering questions — it can observe a situation, decide what to do, take action, and handle the results, including errors and edge cases. Unlike a simple chatbot that responds to individual messages, an agent can work through a multi-step process with branching logic.
In a business context, AI agents handle workflows that are too complex for simple if-then automation but too repetitive to justify a human doing them hundreds of times. A denial management agent, for example, can read a denial reason code, classify the denial type, gather the required documentation from an EHR, draft a payer-specific appeal letter, and queue it for staff review — all without human intervention on the straightforward cases.
Multi-agent systems take this further by having several specialized agents coordinate on a complex workflow. One agent handles intake and classification, another does research, a third makes routing decisions, and others handle execution for different workflow branches.
The key distinction from traditional automation: agents handle variability. They can process unstructured input (like natural language emails or documents), make judgment calls based on context, and handle exceptions gracefully rather than failing when the input doesn't match a rigid pattern.