Building an AI agent on your laptop is one skill. Running it in production on your company’s cloud, with identity, tracing, guardrails, and a budget, is another. AgileFever’s Enterprise AI Agents course, offered for each major cloud, teaches the second one, on Microsoft Azure, Amazon Web Services, or Google Cloud. There’s also a bootcamp that covers all three.
This guide explains what the four programs have in common, how the clouds differ, and which one to pick.
The enterprise AI agents course at a glance
| Azure (Microsoft Foundry) | AWS | Google Cloud | Multi-Cloud BootCamp | |
|---|---|---|---|---|
| Hours | 12, live | 12, live | 12, live | 36, live, over 3 weeks |
| Modules | 6 | 6 | 6 | 11 |
| Next cohort | November 16 to 19, 2026 | November 23 to 26, 2026 | November 30 to December 3, 2026 | November 16 to December 3, 2026 |
| Price | $199 or ₹16,000 | $199 or ₹16,000 | $199 or ₹16,000 | $599 or ₹40,000 |
| Ends with | A final demo | A final demo | A final demo | A capstone demo |
Additional discounts are available. Contact us to know more.
What every single-cloud program covers
The three 12-hour programs follow the same six-module structure, so you learn the same production skills whichever cloud you choose:
| Module | What you do |
|---|---|
| 1. Setup and architecture | Prepare the cloud account, the AI platform, and a budget alert |
| 2. Deploy ML and foundation models | Deploy a pre-trained ML model to a live endpoint and enable foundation models |
| 3. Build LLM applications | Build a RAG application over your own documents, with guardrails and evaluation |
| 4. Build agents | Build single agents with the platform’s own agent tools |
| 5. Multi-agent systems | Coordinate specialist agents, with handoffs, human approval, and A2A communication |
| 6. Deploy, monitor, and operate | Run agents in production with tracing, evaluation, security, and cost controls |
You don’t train models in these programs. The ML model is pre-trained and the foundation models come from their vendors. The work is in deploying, connecting, and operating them, which is what most enterprise teams actually need.
How the three clouds differ
| Azure | AWS | Google Cloud | |
|---|---|---|---|
| Where agents run | Foundry Agent Service | Amazon Bedrock AgentCore | Agent Runtime on Gemini Enterprise Agent Platform |
| How you build agents | Microsoft Agent Framework, with MCP tools | Bedrock Agents (configuration-based) and code-based agents with the Strands Agents SDK | Google’s Agent Development Kit (ADK), code-first in Python |
| Model deployment | Microsoft Foundry | SageMaker AI and Amazon Bedrock | Agent Platform, with Gemini and Model Garden models |
| Safety and security | Tracing, evaluation, security, and cost controls | Guardrails, IAM policies, tracing, and cost controls | Model Armor, identity, tracing, and cost controls |
| Aligned certification | Microsoft Azure AI Apps and Agents Developer Associate (AI-103) | AWS Certified Generative AI Developer, Professional (AIP-C01) | Google Cloud Professional Agentic Architect (in beta as of October 2026) |
A note on names: per the course page, Google moved Vertex AI into Gemini Enterprise Agent Platform in 2026. The Google Cloud program uses the current names and points out the old ones, which helps if your team’s documentation still says Vertex.
Do they prepare you for the certification exams?
Partly, and the course pages are honest about it. Each program covers the hands-on agent and deployment parts of its exam, but not everything. The Azure program covers roughly half of AI-103. The AWS exam is Professional level and needs more practice. The Google program covers the largest parts of its exam but not the low-code tools. If you’re aiming for the exam, plan for the exam-prep add-on and extra practice.
One cloud or the multi-cloud bootcamp?
US prices, October 2026
Source: AgileFever program pages, October 12, 2026.
The total time and price are almost identical, so the choice comes down to what you need. The bootcamp teaches the shared ideas (RAG, agent design, MCP, A2A, guardrails, and evaluation) once at the start, rebuilds the same reference project on each cloud so the differences are easy to see, and ends with a capstone in which agents on different clouds work together through the A2A protocol.
| If you… | Choose |
|---|---|
| Work at a company that’s standardized on one cloud | That cloud’s 12-hour program |
| Are preparing for one specific certification | The matching single-cloud program |
| Work in consulting or pre-sales across clients on different clouds | The Multi-Cloud BootCamp |
| Are an architect comparing platforms for your company | The Multi-Cloud BootCamp |
Who it’s for, and what you need first
These programs are for cloud and backend engineers, AI and ML engineers moving agent prototypes into production, solution architects designing enterprise AI, and DevOps and platform engineers who’ll run agents.
They aren’t beginner courses. Each program asks for the GenAI and Agentic AI BootCamp or equivalent experience building agents, working Python including APIs and SDKs, and a cloud account where you can create resources. Labs use small models and short-lived resources with a budget alert set in the first module; delete the resources after the final demo to stop charges.
FAQ
Can my company run one of these as a private cohort?
Yes. Corporate cohorts are available with flexible scheduling.
What will I have built by the end?
On each cloud: an ML model on a live endpoint, a RAG application with guardrails and evaluation, and a multi-agent system running in production with tracing.
Are sessions recorded?
Yes. Live sessions are recorded.
Program pages
Enterprise AI Agents on Azure, Enterprise AI Agents on AWS, Enterprise AI Agents on Google Cloud, and the Multi-Cloud AI Agents BootCamp.
