"Agentic AI" is the most hyped phrase in software right now, and interviews have noticed. The junior answer to "have you built agents?" is a LangChain demo that worked twice. The senior answer knows why that demo would burn $400 in a loop on Tuesday, why the fix is a step cap and not a better prompt, why tool design matters more than tool count, why the model must never execute anything itself, and what MCP changes about all of it.

This is 50 questions at that second level — the agent loop, planning, tool design, MCP, memory, multi-agent orchestration, guardrails, evals, and production operations. Each with a short answer, a deep dive with real production code, and the senior signal interviewers listen for. The running example is Mattrx Insights — an agentic analytics copilot (110k MAU; C# .NET 9 for orchestration/governance, Python FastAPI for the agent), tools served over MCP by three servers (analytics/read, reports/actions, admin/locked), ~85,000 governed tool calls a day.…