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Answer: In Magentic-One, a lead agent called the Orchestrator plans, tracks progress, and re-plans to recover from errors, directing specialized agents to operate a web browser, navigate local files, or write and execute Python code (S1). A comprehensive survey delivers a systematic review of the field of LLM-based autonomous agents (S2).

Magentic-One (S1)

• Agents must effectively plan, perform multi-step reasoning and actions, respond to novel observations, and recover from errors.
• Magentic-One achieves statistically competitive performance to the state-of-the-art on GAIA, AssistantBench, and WebArena.
• It achieves these results without modification to core agent capabilities or to how they collaborate.
• Its modular design allows agents to be added or removed from the team without additional prompt tuning or training.
• It ships with AutoGenBench, a standalone tool for agentic evaluation with built-in controls for repetition and isolation.

The survey (S2)

• Previous research often trained agents with limited knowledge within isolated environments.
• Through the acquisition of vast amounts of web knowledge, LLMs have demonstrated remarkable potential in achieving human-level intelligence.
• It reviews applications in social science, natural science, and engineering, and the evaluation strategies commonly used.
• The authors maintain a repository of relevant references.

Design notes

The modular design of Magentic-One allows agents to be added or removed, easing development (S1).

Claims

• id: C1 claim: The Orchestrator plans, tracks progress, and re-plans. source: S1 evidence: "the Orchestrator, plans, tracks progress, and re-plans to recover from errors."
• id: C2 claim: Magentic-One is open-source. source: S1 evidence: "We provide an open-source implementation of Magentic-One"
• id: C3 claim: The survey proposes a unified framework. source: S2 evidence: "for which we propose a unified framework that encompasses a majority of the previous work."

Sources

• [S1] [2411.04468] Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks. arXiv. https://arxiv.org/abs/2411.04468 . License: CC BY 4.0. Accessed 2026-11-04.
• [S2] [2308.11432] A Survey on Large Language Model based Autonomous Agents. arXiv. https://arxiv.org/abs/2308.11432 . License: CC BY 4.0. Accessed 2026-11-04.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Summary by iinventedlight (AI-assisted).

@misc{iil_note_gd4byrxa,
  author = {{iinventedlight}},
  title = {{Orchestrator agents: Magentic-One and the LLM-agent landscape}},
  howpublished = {iinventedlight Library},
  year = {2026},
  date = {2026-11-04},
  url = {https://iinventedlight.com/library/notes/ao-handoff-orchestrator-agents/},
  note = {Permanent ID iil:note:gd4byrxa. License: CC-BY-4.0.},
}

Markdown

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