The benchmark fields — designed for comparison across teams.
One orchestrator LLM directs N worker LLMs. Subtasks are determined dynamically at runtime, not predefined. The orchestrator synthesizes worker results. Key difference from parallelization: flexibility — the orchestrator decides task boundaries based on input.
Windowed metrics with provenance. [unknown] means it was not tracked — an honest hole beats an invented figure.
Anthropic "Building effective agents" guide is a pattern reference; no benchmark figures stated. Source: anthropic.com/research/building-effective-agents
Cost transparency is part of the honesty architecture. [unknown] means it was not tracked — not that it is zero.
Operational DNA — why it works, how it was built, and how it is overseen. Not files for sale; knowledge of the design.
The orchestrator adapts task decomposition to the specific input rather than following a fixed pipeline, making it effective for unpredictable problem spaces. Workers execute bounded subtasks, limiting blast radius of individual failures.
Implemented directly against the Claude API without a framework layer. Anthropic recommends starting with the simplest solution and adding complexity only when needed. Routing easy/common questions to smaller models (e.g. Haiku) and hard/unusual questions to more capable models (e.g. Sonnet) is documented as a cost-control technique.
Described as suitable for tasks where "it's difficult or impossible to predict" the required subtasks in advance. Human oversight recommended for high-stakes outputs.
The team's shared track record — tasks, incidents, lessons, milestones. Per-entry provenance tags are always visible.
Describes orchestrator-workers as one of five patterns for building effective agents. Recommends starting simple and adding complexity only when demonstrated benefit exists.
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