From zero to agentic AI engineer

The complete beginner path. No assumptions about prior knowledge. This is the longest path; expect 6-9 months to job-readiness.

What you need to learn first (before the curriculum)

Before you start the agentic AI curriculum, you need:

• Python. Not just syntax - you need to be comfortable with type hints, async/await, classes, and reading library documentation. Spend 4-6 weeks here if Python is new.
• Basic web fundamentals. HTTP, JSON, REST APIs. You will call LLM APIs from day one.
• Command line and Git. You will clone repos, run scripts, commit code. Spend 1-2 weeks here.
• Basic software engineering practices. Functions, modules, testing. Spend 2-3 weeks here.

If any of these are missing, do not skip them. The curriculum assumes them.

Suggested path

Phase 0: Prerequisites (8-12 weeks)

• Python (4-6 weeks): Learn Python the Hard Way, or the official tutorial, or any intro course. Build small projects.
• Web fundamentals (1-2 weeks): HTTP, JSON, REST. Read the MDN web docs.
• Command line and Git (1-2 weeks): Learn Enough Command Line to Be Dangerous, the Git book.
• Software engineering (2-3 weeks): Write tests, use functions and modules, read other people's code.

Phase 1: The curriculum (20-25 weeks)

Now you are ready for the agentic AI curriculum:

1. 01 Foundations - 3 weeks. Go slowly; this is the base.
2. 02 LangGraph core - 4 weeks.
3. 03 Agents in practice - 3 weeks.
4. 04 Tools and MCP - 2 weeks.
5. 05 Agentic patterns - 3 weeks.
6. 06 Evals and observability - 2 weeks.
7. 07 Multi-agent and A2A - 2 weeks.
8. 08 Production - 3 weeks.

Phase 2: Portfolio (4-6 weeks, in parallel with the curriculum)

Start building a portfolio project as soon as you finish module 03. Do not wait until the end. Pick a project from projects/ that excites you and build it incrementally as you learn.

Timeline

6-9 months at 2-3 hours per day. This is the longest path because you are starting from scratch. Do not rush; the goal is job-readiness, not speed.

Your advantage

You do not have one yet. The advantage you will build is the portfolio - a working agent, deployed, with evals, that demonstrates you can do the job. The portfolio matters more than the resume, more than the certifications, more than the degree. Build it well.

Common mistakes for this transition

• Skipping the prerequisites. Why: "I will learn Python as I go." Fix: do not. You will struggle with the curriculum and give up.
• Rushing the foundations. Why: you want to build agents. Fix: the foundations are the base; without them, everything else is shaky.
• Not building a portfolio. Why: "I will build it after I learn everything." Fix: build it as you learn. The portfolio is the proof that you learned.
• Not asking for help. Why: "I should figure it out myself." Fix: use GitHub Discussions, Discord, Reddit. The community is helpful.

Projects to build first

• Project 01: Research agent - the simplest project; a good first build.
• Project 02: Customer support multi-agent - a more ambitious project; build this after the first one.

Next steps

After you finish the path:

• Read the field guide for career guidance for new agentic AI engineers.
• Ship your portfolio project. Deploy it. Make it publicly accessible.
• Apply for junior "AI Engineer" roles. Emphasize the portfolio; it is your differentiator.
• Keep learning. The field evolves fast; the curriculum's 09 Emerging topics module will keep you current.