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 117 interview questions with model answers and code.

Try a mock interview

You get a random question from the interview bank. Answer it out loud before the timer runs out, then compare with the model answer.

2:00
Loading a question…

Think out loud: definition → how it works → trade-offs → an example from your work.

Browse all 117 questions, filters and flashcards →

What's inside

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

🧠

Agent Fundamentals

LLMs, tool calling, ReAct, planning, memory, RAG, multi-agent patterns and when not to use an agent.

~45 min read
🔀

Agent, Orchestration & Ops Patterns

Twenty-three patterns interviewers ask about, as animated flow diagrams: ReAct, supervisor, orchestrator-workers, A2A, durable execution, guardrails, scaling…

23 animated GIFs · copy or download
🕸️

LangGraph Deep Dive

State, nodes, edges, reducers, checkpointers, human-in-the-loop, streaming, subgraphs and supervisors.

~60 min · code-first
🛠️

Real-World Project

Build "SupportPilot": a customer-support agent with tools, RAG, Postgres memory, approvals, FastAPI, Docker and tests.

12 steps · ~3–4 hours
☁️

Deploy to GCP

Cloud Run, Artifact Registry, Cloud SQL, Secret Manager and GitHub Actions with Workload Identity Federation.

Step by step
🟧

Deploy to AWS

ECR, ECS Fargate behind an ALB, RDS Postgres, Secrets Manager, CloudWatch and GitHub OIDC.

Step by step
🔷

Deploy to Azure

Azure Container Apps, ACR, PostgreSQL Flexible Server, Key Vault, managed identity and GitHub Actions.

Step by step
🛡️

Agent Ops

Orchestration patterns, state & monitoring, scaling, failure recovery and guardrails, with real-world incidents and interview questions.

Runnable code · interview corner
🎯

Interview Q&A

Searchable, filterable bank of questions with model answers, from fundamentals to system design. Includes flashcard mode.

117 questions · flashcards
🧭

FDE Interview Prep

Forward Deployed Engineer loop, most-asked questions, the decomposition case framework, 8 customer use cases and a mock FDE interview.

15 FDE questions · animated case framework
💼

Get Hired

Pick the right AI role, follow a 30-day plan, build a portfolio, get referrals and prepare for every interview stage.

30-day plan · templates

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 and watch the animated design patterns. 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. Throughout — Run your job search

    Follow the Get Hired 30-day plan alongside the learning: positioning, referrals, applications and a weekly review.

  7. 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.