Expired50% confidenceFactExact time
In a multi-agent architecture, the Orchestrator is responsible for task decomposition and scheduling, delegating specific subtasks to specialized Sub-Agents for parallel or sequential execution; this is the design pattern of mainstream AI Agent frameworks such as AutoGPT, LangGraph, and OpenAI Swarm.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
This claim has expired. It is kept for the record and no longer describes the current state.
Valid until: 9/30/2026(expired)
Sources
35 Lines of Prompts Let Codex Auto-Optimize Your Workflow — Reposted by OpenAI's President
bilibili量子位Daily5/31/2026
Related Claims
UnverifiedIn a layered agent architecture, the top-level Orchestrator Agent is responsible for task decomposition and decision-making, while the bottom-level Worker Agent is responsible for specific code generation and test execution, representing the best practice for AI application engineering.81% similarVerifiedThe Multi-Agent architecture draws on microservices thinking, with an Orchestrator responsible for task distribution and result aggregation, enabling parallel execution to reduce latency and precise retry of individual sub-Agents.81% similarUnverifiedIn the Orchestrator-Worker architecture, the coordinator Agent is responsible for task planning and distribution while the executor Agent focuses on specific subtasks; both AutoGen's GroupChat and LangGraph's StateGraph support this pattern.81% similarUnverifiedAgent Orchestration refers to the mechanism by which a central scheduler coordinates multiple specialized AI agents to collaboratively complete complex tasks, originating from the microservices architecture concept in distributed systems79% similarVerifiedThe core architecture of multi-agent systems typically involves an Orchestrator agent responsible for task decomposition and scheduling, along with multiple Worker agents responsible for specific implementation.78% similar
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