Written by the AgileFever editorial team. Program details were checked against the live program pages in October 2026. We teach the full stack course mentioned below.
“AI solutions engineer” covers two different jobs. One is using AI to ship ordinary software faster and with fewer defects. The other is building products that have a language model inside. A full stack developer can move toward either, and the sensible route is different for each. This roadmap separates them so you do not buy a 74-hour course when a 12-hour one would do, or the reverse.
Two paths from full stack developer
| Path A: AI-assisted developer | Path B: AI solutions engineer | |
|---|---|---|
| What you do | Build conventional features faster, review AI-written code, own quality | Build products with LLM features: RAG, agents, tool calling, evaluation |
| Core skills | Prompting coding tools, code review, security checks, test generation, debugging with AI | Retrieval design, agent frameworks, structured outputs, evaluation, deployment, safety |
| AgileFever program | Full Stack Developers course: 12 hours, $450 | GenAI and Agentic AI BootCamp: 74 hours, $2,000 |
| Prerequisite | Working knowledge of a modern stack, Git, code review, testing, and Python for AI (6 hours) | Comfortable reading and writing code; a refresher module comes with registration |
| Proof of skill | Applied final exercise (fix a seeded bug and extend your feature) plus a short test | Capstone project, no exam |
Additional discounts are available. Contact us to know more.
What path A teaches
GenAI & Agentic AI for Full Stack Developers has six modules: three generative (frontend components, backend logic and database, technical documentation) and three agentic (code review and security, testing, and debugging and root cause analysis). Tools are GitHub Copilot, Cursor and Claude Code. You build one running feature across all modules, with frontend, backend, tests and docs. Four of the six modules concern checking and fixing code rather than writing it, which fits where AI-generated code goes wrong.
It deliberately does not teach you to build RAG apps, agents or LLM features.
A review habit worth keeping on either path
| Check | What to look for in generated code |
|---|---|
| Inputs | Is user input validated and escaped? |
| Errors | What happens on a timeout, an empty result or a failed write? |
| Secrets | Are keys hard-coded, logged or sent to the client? |
| Tests | Do the tests check behaviour, or only that the code runs? |
| Fit | Does it follow your codebase, or add a new library for one function? |
What path B teaches
The GenAI and Agentic AI BootCamp runs 74 hours over 8 weeks: 64+ live hours and 10 self-paced. It covers prompt and context engineering, advanced RAG, fine-tuning, agent architectures and frameworks (LangGraph, CrewAI, AutoGen), memory, planning, multi-agent systems, evaluation, security, and deployment. Full Stack Developer and Backend Developer are both listed as starting roles, with Application Developer as one of the target roles on the program page. You finish with a capstone from eight options.
Hours listed on the program pages, excluding practice
A sensible order
- Spend a month using one AI coding tool on real work and note where its output needed fixing.
- If the fixes are about quality (missing validation, weak tests), take the short course. If you find yourself wanting to build the AI parts, plan for the bootcamp.
- Whichever you take, finish with something you can show: a repository, a pull request history, or a deployed capstone.
Neither path guarantees a particular title. They give you skills and a portfolio, and hiring decisions remain with employers.
