← 🤖 Retrieval & AgentsNEXT IN RETRIEVAL & AGENTSAgent Reliability and Long-Horizon Robustness→
Core
Multi-Agent Orchestration
When a task is too big or varied for one agent, an orchestrator decomposes it and delegates 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 within its window) plus parallelism and specialization. The costs are coordination overhead, latency, and error propagation, so you use multiple agents only when the task genuinely needs it. Applied-AI interviews probe it because multi-agent designs are common and easy to over-apply.
a free account unlocks the core curriculum tier · no card
RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
RAG & Agent System DesignWhen do you use a multi-agent system, and what orchestration patterns and pitfalls matter?→RAG & Agent System DesignWhat are sub-agents, and how does an orchestrator delegate to them effectively?→RAG & Agent System DesignHow do agents communicate and coordinate in a multi-agent system?→RAG & Agent System DesignWhat protocols govern how agents hand off work, and what makes multi-agent coordination break?→RAG & Agent System DesignWhen do you build an agent instead of a single LLM call, and how do you keep a multi-step agent reliable?→RAG & Agent System DesignWhat is the Plan-and-Execute agent pattern, and how does it compare to ReAct?→
COMPANIES THAT ASSUME THIS