Unverified50% confidenceFactExact time
Ultra mode adopts a multi-agent parallel architecture in which an Orchestrator is responsible for task decomposition and dependency analysis, Worker Agents concurrently execute independent subtasks, and an Aggregator consolidates results, reducing overall completion time from O(n) to close to O(max_subtask).
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50%
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Long-term
Relevance
7/23/2026
First Seen
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Related Claims
VerifiedThe 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% similarUnverifiedIn 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.77% 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.76% similarUnverifiedIn 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.73% similarUnverified在 Ultra 模式下，Codex 会自动将任务拆分给多个子智能体（sub-agents）并行执行，无需用户手动干预72% similar
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