This post describes how AgileFever’s courses reflect changes in AI tools and roles. Each example comes from our published program pages, so you can check it yourself.
AI changes faster than almost any subject people pay to learn, so an AI course curriculum has to keep moving. Platforms get renamed, new certifications appear, and frameworks that were standard a year ago get replaced. A course that doesn’t change with them teaches you the past. Here’s how that shows up in what we teach today, with specific examples.
Five ways our AI course curriculum keeps up
1. Following platforms when they change
Google moved Vertex AI into Gemini Enterprise Agent Platform in 2026. Our Enterprise AI Agents on Google Cloud program teaches the current platform and names, and points out the old names, because many teams’ documentation still uses them. The same applies on Azure, where the program builds on Microsoft Foundry and the Microsoft Agent Framework, and on AWS, where it covers both Bedrock Agents and AgentCore.
2. Aligning with new certifications, and saying where they fall short
Our cloud agent programs are aligned with the newest agent-focused exams: Microsoft’s AI-103, the AWS Certified Generative AI Developer Professional (AIP-C01), and Google Cloud’s Professional Agentic Architect, which was in beta as of October 2026. Each program page states which parts of the exam the course covers and which it doesn’t, and recommends extra preparation before booking.
| Program | Aligned certification | What the page says it covers |
|---|---|---|
| Enterprise AI Agents on Azure | AI-103 | The generative AI and agent domain and part of planning, roughly half the exam |
| Enterprise AI Agents on AWS | AIP-C01 | Bedrock, RAG, guardrails, agents, and deployment at a working level; a Professional exam needs more practice |
| Enterprise AI Agents on Google Cloud | Professional Agentic Architect (beta) | Custom agents and deployment, the largest parts of the exam; not the low-code tools |
3. Extending older disciplines to AI
MLOps didn’t disappear when large language models arrived; it grew. Our MLOps BootCamp now ends with LLMOps and Agentic AI Ops modules, so the same versioning, deployment, and monitoring discipline gets applied to LLM applications and agents.
4. Teaching the protocols agents now depend on
Agents increasingly talk to tools and to each other through shared protocols. The GenAI and Agentic AI BootCamp has a module on agent communication protocols (MCP, A2A, and ACP), and the Multi-Cloud AI Agents BootCamp ends with a capstone where agents on different clouds work together through A2A.
5. Building courses around roles, not tools
Most people don’t need to become AI engineers. They need AI to change how they do the job they have. That’s why we run role-based courses for business leaders, architects, product owners and managers, project and program managers, full stack developers, DevOps engineers, and QA engineers, each built around that role’s real tasks.
| Role course | Built around |
|---|---|
| Project & Program Management | Six working tools, from a charter generator to a weekly status report |
| QA and Test Engineers | Testing with AI, and testing AI products with DeepEval, RAGAS, and Promptfoo |
| Solution Architects | An architecture package for a use case at your own company |
| Business Leaders | An AI transformation playbook presented to a mock executive panel |
What we try not to do
Keeping up doesn’t mean chasing every new tool. We’d rather teach the patterns that outlast a framework, like retrieval, evaluation, guardrails, and human review, and use current tools to practice them. A course that teaches only the newest library is out of date as soon as that library changes.
How to tell if any course is current
| Check | What to look for |
|---|---|
| Platform names | Does it use the current names for cloud AI services? |
| Certifications | Is it aligned with current exams, and honest about what it doesn’t cover? |
| Evaluation | Does it teach how to test AI output as well as how to generate it? |
| Production | Does it cover deployment, monitoring, and cost, or stop at a demo? |
What to read next
For how we teach, read AgileFever’s AI teaching philosophy. For what each program asks you to build, see Inside AgileFever capstones.
