LLM orchestration refers to the coordination of multiple language model agents through defined roles and control flow, rather than relying on a single model to handle an entire task end-to-end. The two sources here converge on a GAN-inspired decomposition: a planner that breaks work into subtasks, generators that execute in parallel, and evaluators that critique outputs before they advance downstream.

Anthropic engineer Prithvi Rajasekaran describes a harness architecture built for multi-hour autonomous coding sessions Harness Design for Long-Running Application Development. The core problems it addresses are context anxiety (models losing coherence as context windows fill) and self-evaluation bias (models rating their own outputs too favorably). Separating the generator and evaluator roles breaks the feedback loop that causes self-flattery, while an explicit planner keeps the task decomposition stable across a long session.

The orchestrator-supaconductor project applies a similar structure as a Claude Code plugin Ibrahim-3d/orchestrator-supaconductor, adding a virtual Board of Directors layer for high-stakes architectural decisions. A single natural-language command triggers planning, parallel agent execution, quality evaluation, and board review, encoding the orchestration pattern into a reusable pipeline.

The consistent theme across both sources is role specialization. Orchestration gains its value precisely because no single model call is trusted to plan, execute, and judge simultaneously.