Enterprises seeking to deploy numerous AI agents must establish a framework to manage them. Microsoft researchers recently introduced a new multi-agent infrastructure called Magentic-One. This system enables a single AI model to power various helper agents working together to complete complex, multi-step tasks in different scenarios. The Magentic-One framework is open-source and available for researchers and developers, including commercial use, under a custom Microsoft License. In addition, Microsoft released an open-source agent evaluation tool called AutoGenBench. The goal of generalist agentic systems is to understand how autonomous agents can solve multi-step tasks, which are often found in the daily operations of an organization.
From examples provided by Microsoft, it appears that Magentic-One is designed to handle routine tasks. Magentic-One relies on an Orchestrator agent that directs and manages four other agents. These agents include Websurfer, FileSurfer, Coder, and ComputerTerminal. The Orchestrator creates a task ledger to track the progress and workflow of the agents. Microsoft designed Magentic-One using OpenAI’s GPT-4o, but the system is LLM-agnostic. It supports multiple models behind the agents, providing developers with the flexibility to deploy various models for different tasks. This integration of agentic systems has led to a competition among tech companies to develop AI orchestration frameworks that can manage these workflows. As more enterprises begin using AI agents, managing that sprawl and ensuring seamless task completion is becoming increasingly crucial.
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