In one sentence
A system that uses a model and tools to pursue a goal across multiple steps within defined permissions and review boundaries.
An AI agent combines a model with instructions, tools, state, and an execution loop. It can interpret variable input, select an allowed action, inspect the result, and continue until it reaches a stopping or escalation condition.
Agents are useful when inputs are difficult to express as fixed rules, such as classifying documents, routing messages, or assembling a draft from several systems. They are not automatically autonomous or reliable. Tool permissions, representative evaluation, deterministic validation, monitoring, and human review determine how much of a workflow can safely run without intervention.
A denial-management workflow, for example, might classify a denial code, gather approved records, and draft an appeal package for qualified staff review. It should not silently decide which claims deserve appeal or submit unsupported clinical assertions.
Multi-agent systems divide work across narrower roles. That can improve separation of responsibilities and testing, but it also adds coordination, latency, cost, and more failure paths. Start with one agent unless distinct roles create a measurable benefit.
Keep following the system
Model Context Protocol (MCP)
An open standard for connecting compatible AI applications to tools, resources, and prompts.
Workflow Automation
Connecting business systems so repeatable tasks run according to defined rules and exceptions reach the right person.
Multi-Agent Orchestration
Coordinating multiple specialized agents when distinct roles and handoffs improve a complex workflow.