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Core
Multi-Agent Orchestration
When a task is too big or varied for a single agent, an orchestrator breaks it apart and hands subtasks to focused sub-agents, each with its own clean context and tools, then synthesizes the results. The main benefit is context isolation (each sub-agent stays focused and inside its window) alongside parallelism and specialization. The costs are coordination overhead, latency, and error propagation, so you reach for multiple agents only when the task truly needs it. Applied-AI interviews test it because multi-agent designs are common and easy to over-apply.
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RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
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