context engineering
Applied AI interview questions tagged context engineering, across every topic.
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Concepts behind "context engineering"
The curriculum that explains the ideas these questions test.
Core
Context Engineering for AgentsContext engineering is the discipline of designing the full information payload that goes into an agent's context window each turn: system instructions, memory, retrieved data, tool definitions and results, and conversation history. Most agent failures are context failures, where the right information is absent, buried, stale, or crowding out the rest of the budget. Applied AI interviews probe it because it is the highest-leverage lever on agent reliability and cost, and it separates people who tune prompts from people who manage state.🤖 Retrieval & AgentsSign in
Core
Multi-Agent OrchestrationWhen 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.🤖 Retrieval & AgentsSign in