Tokenization Architecture Interview Questions

Cursor rules are context engineering in disguise.

**Proxy layer** Backend holds keys; assigns budgets per customer/plan.

500M vectors at sub-100ms p99 is a staff-level vector search design question from Uber, Airbnb, and large-scale ML platform teams. Learn sharding strategies, index tuning, and the operational trade-offs that separate senior from principal engineers.

A production AI coding agent typically includes:

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.

**Session model** WebSocket/WebRTC streaming multimodal input/output.

**Session** Persistent realtime connection with session.update for instructions/tools; handle reconnect.

Rate limiting for agents differs from human API usage — bursts are larger and less predictable.

Multi-server hosts are production reality — GitHub + Jira + DB + docs + internal APIs. Poor design creates tool soup.

Enterprise MCP rarely connects laptops directly to SaaS MCP servers. The gateway is the control plane interviewers want you to design.

Building a custom MCP server is a common system-design follow-up to "what is MCP?" Strong candidates describe end-to-end architecture, not just SDK boilerplate.

Horizontal scaling patterns: hash sharding, semantic partitions, routing embeddings, and merge/rerank at query time.

Label definitions, calibration examples, abstain classes, and structured outputs for production classifiers without fine-tuning.

Protect instructions, secrets, policy, and tool policies in system prompts against leakage, override, and indirect injection.

Medium AI Agents interview question on tool schema design and versioning — architecture, trade-offs, eval, and production patterns.

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 swarm vs supervisor multi-agent design — architecture, trade-offs, eval, and production patterns.

Medium AI Agents interview question on idempotent tool execution — 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 AI Agents interview question on tool calling design best practices — architecture, trade-offs, eval, and production patterns.