Agentic AI Engineer Interview Prep

Agentic AI Engineer preparation roadmap

Follow these concepts in order. Each opens its guide, interview QA, and practice MCQs while keeping this role as your study context.

1. 1

   Core Data Structures

   Lists, tuples, dicts, and sets — their underlying implementations and when each is the right choice.

2. 2

   Comprehensions & Generators

   Concise, often faster ways to build sequences — and the lazy-evaluation alternative that avoids materializing them at all.

3. 3

   OOP & Data Classes

   Classes, inheritance, and the @dataclass shortcut for the common case of a class that's mostly just data.

4. 4

   Decorators & Context Managers

   Wrapping a function's behavior without changing its code, and guaranteeing setup/teardown runs even when something fails.

5. 5

   Concurrency (GIL, Threading, Asyncio)

   Why Python threads don't parallelize CPU work, and the two real ways around it: multiprocessing and asyncio.

6. 6

   LLM Fundamentals

   The core mechanics of how LLMs work and the practical parameters engineers actually tune: attention, tokenization, sampling, structured output, and cost/latency trade-offs.

7. 7

   Prompt Engineering & Prompt Management

   Designing reliable prompts and treating them as versioned, tested production artifacts rather than one-off strings.

8. 8

   Retrieval-Augmented Generation (RAG)

   Grounding an LLM's output in retrieved external documents instead of relying purely on its trained-in knowledge.

9. 9

   Advanced RAG & Retrieval Quality

   The retrieval-quality techniques that separate a working RAG demo from a production system that reliably surfaces the right context.

10. 10

    LLM Agents & Tool Use

    Letting an LLM decide which actions to take — calling tools, APIs, or other models — rather than just generating text.

11. 11

    Multi-Agent Systems & Orchestration

    Coordinating multiple specialized LLM agents to handle work that's too complex or too poorly-decomposed for a single agent loop.

12. 12

    Agent Memory & Context Management

    Giving agents state across turns and sessions despite a fixed, finite context window.

13. 13

    Fine-Tuning & Adaptation

    Adapting a pretrained LLM's behavior via further training, and how that differs from prompting or RAG.

14. 14

    LLM Evaluation

    Measuring whether an LLM-based system actually works, given that outputs are open-ended and hard to score automatically.

15. 15

    LLM Observability & Monitoring

    Monitoring, logging, and tracing LLM and agent systems in production — the specific signals and tooling that apply on top of general observability practice.

16. 16

    LLMOps: Deployment, Versioning & Cost Management

    Safely shipping changes to prompts, models, and RAG configuration in production, and keeping the resulting system's cost under control.

17. 17

    Troubleshooting LLM, RAG & Agent Systems

    A practical diagnostic playbook for the specific ways LLM, RAG, and agent systems fail in production.

18. 18

    Agent Frameworks, Orchestration & MCP

    Stateful agent orchestration frameworks (LangGraph and peers) and MCP, the emerging standard protocol for connecting models to tools and data.

19. 19

    AI Security, Governance & Responsible AI

    The security and governance concerns specific to LLM systems: prompt injection, data leakage, access control over retrieved content, and responsible-use practices.

20. 20

    Scalability Fundamentals

    Production scalability fundamentals for technical interviews: bottlenecks, scaling, load balancing, autoscaling, capacity, overload control, and failure behavior.

21. 21

    Caching Strategies

    Production caching for technical interviews: placement, read/write patterns, freshness, stampedes, HTTP caching, observability, failure recovery, and decision tradeoffs.

22. 22

    Database Scaling (Sharding & Replication)

    Splitting data across machines (sharding) and copying it across machines (replication) — solving two different scaling problems.

23. 23

    Message Queues & Async Processing

    Decoupling a slow or unreliable step from the request path by handing it to a queue and processing it separately.

24. 24

    CAP Theorem & Consistency Models

    Why a distributed system can't have perfect consistency, availability, and partition tolerance all at once — and what real systems trade off.

25. 25

    API Design & REST Fundamentals

    Designing HTTP APIs that are predictable to call and safe to retry — resource modeling, status codes, versioning, and idempotency.

26. 26

    API Authentication & Authorization

    Verifying who's calling an API (authentication) and what they're allowed to do (authorization) — API keys, OAuth, and JWTs.

27. 27

    Webhooks & Asynchronous API Integration

    Handling work that can't complete within a single request/response cycle — inbound webhooks and long-running async job APIs.

28. 28

    URL Shortener Design

    Designing a URL shortener: unique keys, redirect semantics, cache TTLs, click accounting off the GET path, and open-redirect abuse.