Multi-Agent Framework
A system where multiple specialized AI agents with different roles collaborate to solve complex problems together.
In Plain English
A multi-agent framework is like assembling a team of specialists, each with a particular expertise, to tackle a difficult challenge. Rather than one general-purpose AI handling everything, multiple focused agents work together—one might gather information, another might analyze it, a third might make decisions, and a fourth might communicate results. Each agent has its own goals and responsibilities but coordinates with the others to reach a shared outcome. This approach mirrors how human teams work and is particularly powerful for problems that require different kinds of expertise or judgment applied in sequence.
💡Real-World Example
A hospital system uses a multi-agent framework for generating radiology reports. One agent reviews the medical imaging, another checks the patient's history, a third considers common diagnoses, and a fourth drafts the final report—with each agent communicating its findings to the others. This collaborative approach catches errors and produces more thorough, accurate reports than a single AI could.
Related Terms
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