Free · Hands-on · Interview-focused

Interview prep for the AI era

Prepare for AI agent interviews end to end: learn agent fundamentals and LangGraph, build a production agent, deploy it to GCP, AWS or Azure, and practise 59 interview questions with model answers and code.

What's inside

Six tracks. Do them in order or jump straight to what you need.

Suggested 4-week roadmap

About 6–8 hours per week. Tick off steps as you go — progress is saved in your browser.

  1. Week 1 — Foundations

    Read Fundamentals. Write a raw tool-calling loop in ~50 lines of Python without any framework so you understand what frameworks hide.

  2. Week 1–2 — LangGraph

    Work through LangGraph: build a ReAct graph by hand, add a checkpointer, an interrupt, and stream tokens.

  3. Week 2–3 — Build SupportPilot

    Follow the project: tools, RAG, approvals, API, Docker, evals. Push it to your GitHub — it's your portfolio piece.

  4. Week 3 — Deploy to one cloud (then a second)

    Pick the cloud your target employer uses: GCP, AWS or Azure. Then skim the other two so you can compare them in an interview.

  5. Week 3–4 — Run it like production

    Work through Agent Ops: durable execution, monitoring, capacity maths, failure recovery and layered guardrails. This is what separates senior candidates.

  6. Week 4 — Interview prep

    Drill the Q&A bank in flashcard mode. Practise the system-design questions out loud, using your project as the worked example.

The project you'll build

One codebase, three clouds.

Web / Chat UI FastAPI/chat · /approve · SSEcontainer LangGraph agentrouter → agent ⇄ toolshuman approval (interrupt)guardrails LLM API Tools: orders, tickets Vector store (KB) Postgres checkpoints
SupportPilot: a stateful support agent with tool use, retrieval, human-in-the-loop and durable memory.