New AI Multi-Agent System // Magentic-One
An attempt to create universal predefined multi-agent system for wide range of tasks
We are introducing Magentic-One, our new generalist multi-agent system for solving open-ended web and file-based tasks across a variety of domains. Magentic-One represents a significant step towards developing agents that can complete tasks that people encounter in their work and personal lives. We are also releasing an open-source implementation of Magentic-One(opens in new tab) on Microsoft AutoGen, our popular open-source framework for developing multi-agent applications.
Magentic-One employs a multi-agent architecture where a lead agent, the Orchestrator, directs four other agents to solve tasks. The Orchestrator plans, tracks progress, and re-plans to recover from errors, while directing specialized agents to perform tasks like operating a web browser, navigating local files, or writing and executing Python code.
Overall, Magentic-One consists of the following agents:
Orchestrator: the lead agent responsible for task decomposition and planning, directing other agents in executing subtasks, tracking overall progress, and taking corrective actions as needed
WebSurfer: This is an LLM-based agent that is proficient in commanding and managing the state of a Chromium-based web browser. With each incoming request, the WebSurfer performs an action on the browser then reports on the new state of the web page The action space of the WebSurfer includes navigation (e.g. visiting a URL, performing a web search); web page actions (e.g., clicking and typing); and reading actions (e.g., summarizing or answering questions). The WebSurfer relies on the accessibility tree of the browser and on set-of-marks prompting to perform its actions.
FileSurfer: This is an LLM-based agent that commands a markdown-based file preview application to read local files of most types. The FileSurfer can also perform common navigation tasks such as listing the contents of directories and navigating a folder structure.
Coder: This is an LLM-based agent specialized through its system prompt for writing code, analyzing information collected from the other agents, or creating new artifacts.
ComputerTerminal: Finally, ComputerTerminal provides the team with access to a console shell where the Coder’s programs can be executed, and where new programming libraries can be installed.
