Tokenization System Design Interview Questions

Multi-agent orchestration is a senior/staff-level system design question gaining traction at Google DeepMind and Microsoft. Learn the supervisor pattern, shared state management, and how to avoid the 'too many agents' anti-pattern that sinks most candidate answers.

Cursor rules are context engineering in disguise.

This is an enterprise platform design question spanning agents, MCP, governance, and observability.

Advanced scenario question on why LLMs hallucinate — next-token prediction vs fact verification, root causes, and production defenses asked at OpenAI, Google, Meta, Anthropic, Microsoft, and Amazon.

Part 2 of the LLM hallucination handbook — factual, citation, reasoning, math, code, temporal, context, and tool-use hallucinations; decoding risks; RAG failure modes; production case study.

**Graph structure** draft → auto_checks → legal_subgraph → manager_subgraph → publish; reject edges loop to draft with comments in state.

**State schema** ticket_id, customer_tier, messages, retrieved_docs, proposed_actions, sentiment, escalation_reason, resolution_code.

MCP auth interviews test whether you treat agents as privileged automation — because they are.

Medium AI Agents interview question on agent tracing and replay debugging — architecture, trade-offs, eval, and production patterns.

Medium AI Agents interview question on agent cost caps and budget controllers — architecture, trade-offs, eval, and production patterns.

Hard AI Agents interview question on agent permissions and least privilege — architecture, trade-offs, eval, and production patterns.

Hard AI Agents interview question on customer support agent system design — architecture, trade-offs, eval, and production patterns.

Medium AI Agents interview question on stopping criteria and infinite loop prevention — architecture, trade-offs, eval, and production patterns.

Hard AI Agents interview question on multi-agent debate and critique patterns — architecture, trade-offs, eval, and production patterns.

Medium RAG interview question on end-to-end rag observability — architecture, trade-offs, eval, and production patterns.

Medium RAG interview question on cost of embeddings at 100m documents — architecture, trade-offs, eval, and production patterns.

Medium RAG interview question on compression and contextual distillation — architecture, trade-offs, eval, and production patterns.

Hard RAG interview question on self-rag and corrective rag — architecture, trade-offs, eval, and production patterns.

Medium RAG interview question on agentic rag vs naive rag — architecture, trade-offs, eval, and production patterns.

Hard RAG interview question on rag latency budget: sub-second design — architecture, trade-offs, eval, and production patterns.

Medium RAG interview question on hybrid search: bm25 + dense vectors explained — architecture, trade-offs, eval, and production patterns.

Medium RAG interview question on chunking strategies for different document types — architecture, trade-offs, eval, and production patterns.

Scenario question on cross-model tokenizer bugs — chunk boundaries, context overflow, and embedder vs LLM alignment.

Scenario question on abstention — retrieval scores, calibration, UX of uncertainty, and avoiding confident wrong answers.