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Magentic-One 🤖 – Open-Source Multi-Agent Framework for Generalist Autonomous Workflows

Updated: Aug 26

Featured image of the Microsoft logo representing the Magentic-One listing in the AI Solutions Directory at newbits.ai – open-source multi-agent framework for generalist autonomous workflows.

Magentic-One is an open-source multi-agent system developed by Microsoft, built on the AutoGen framework. It orchestrates specialized agents—WebSurfer, FileSurfer, Coder, ComputerTerminal, and the Orchestrator—to execute complex, multi-step tasks across web, file, and code environments. This modular system delivers generalist agentic capabilities without requiring prompt tuning for agent addition or removal.


🧠 How Magentic-One Elevates Agentic AI Flexibility


Magentic-One delivers robust adaptability through its modular, generalist architecture. Each agent operates independently yet collaboratively under the Orchestrator’s direction, enabling streamlined planning, error recovery, and interactive management. Its open-source design promotes extensibility and developer innovation within the AutoGen ecosystem.


🔍 Key Features at a Glance


  • Modular Multi-Agent Design – Orchestrator coordinates WebSurfer, FileSurfer, Coder, and ComputerTerminal agents


  • AutoGen-Powered Architecture – Built on Microsoft’s open-source AutoGen framework for dynamic agent orchestration


  • Benchmark-Proven Performance – Demonstrated competitive results on GAIA, AssistantBench, and WebArena through AutoGenBench


  • Flexible Agent Management – Add or remove agents without prompt tuning for scalable workflows


  • Structured Planning & Error Recovery – Task and progress ledgers enable re-planning and reliability


  • Human Oversight & Safety – Designed with compliance, transparency, and responsible AI practices


🚀 Real-World Use Cases for Magentic-One


  • Automating multi-step workflows like data extraction, code execution, and web navigation


  • Complex development, analytics, or research tasks where distinct skills must interact


  • Resilient task orchestration that adapts to errors and recovers mid-execution


  • Evaluating and benchmarking agentic systems with AutoGenBench


📌 Example Scenario


A research team uses Magentic-One to automate an end-to-end workflow. The Orchestrator assigns tasks—FileSurfer retrieves datasets, Coder analyzes them, ComputerTerminal executes models, and WebSurfer fetches supporting sources. If a step fails, the Orchestrator dynamically replans, ensuring successful completion. All results are tracked and auditable, enabling both reliability and safety in enterprise deployment.



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