Most generative AI training for architects teaches you to use AI: draft a document faster, summarize an RFP, generate a diagram. That helps, but a good AI solution architect course targets the part of the job that is changing. The bigger change is that architects are now expected to design systems with AI inside them, and to defend those designs on cost, security, and how they fit with what the company already runs.
GenAI & Agentic AI for Solution Architects is a 16-hour course built around that second job. This guide covers what it teaches, who it suits, and where its limits are.
The AI solution architect course at a glance
| Format | Live online, three consecutive days |
| Hours | 16 |
| Modules | 10, ending in a capstone |
| Coding during the course | None |
| Prerequisites | Python for AI pre-work (6 hours), architecture fundamentals, comfort with at least one cloud platform. No prior generative AI experience needed |
| Capstone | An architecture package for a real use case at your own company |
| Current price | $450 or ₹25,000 |
Additional discounts are available. Contact us to know more.
Using AI vs architecting AI
The course FAQ is direct about how the time is split. A small part covers using AI in your own architecture work. Most of it covers designing AI systems.
Approximate hours, as described in the course FAQ
Source: AgileFever course FAQ, October 2026. Remaining hours go to the platform decision framework and the capstone.
What the ten modules cover
| # | Module | The question it answers |
|---|---|---|
| 1 | Platform and vendor decision framework | Which model provider and platform fits this use case, and why |
| 2 | Architecture docs and RFP automation | Where AI saves an architect time today |
| 3 | RAG pipeline architecture | How to ground a model in company data reliably |
| 4 | Multi-agent architecture | When agents are worth their complexity, and how to structure them |
| 5 | LLMOps and production deployment | How AI systems get versioned, evaluated, and deployed |
| 6 | FinOps cost modeling | What the system will cost at real traffic, before you commit |
| 7 | AI security architecture | How to handle prompt injection, data leakage, and access control |
| 8 | Legacy integration patterns | How AI connects to the systems already in place |
| 9 | Governance, evaluation, and rollout | How to get a design approved and roll it out safely |
| 10 | Capstone | An architecture package for your own company’s use case |
Modules 6 to 8 are the ones architects tend to say they couldn’t find elsewhere. Cost modeling, security, and legacy integration are where AI designs get rejected in review, and they’re the parts most tutorials leave out.
The capstone
You produce an architecture package for a real use case at your company: the design, the platform choice and its reasoning, a cost model, a security approach, and an integration plan. Because it’s based on your own environment, it often turns into the first draft of a real proposal.
Who it fits
- Solution architects who are being asked to design AI features and want a structured way to do it
- Enterprise architects setting AI standards and reference architectures
- Pre-sales and technical consultants who need to propose AI solutions credibly
- Senior engineers moving into an architecture role with AI in scope
If you want to see what AI looks like in an architect’s day-to-day work first, GenAI and agentic AI use cases for the enterprise architect role is a good starting point.
Who should take something else
| If you want to… | A better fit |
|---|---|
| Build RAG pipelines and agents yourself, in code | GenAI and Agentic AI BootCamp (74 hours) |
| Design and deliver AI at client sites | AI Forward Deployed Engineer BootCamp (90 hours) |
| Decide AI strategy and investment, not system design | AI Transformation for Business Leaders (16 hours) |
| Learn how to operate ML systems in production | MLOps BootCamp (60 hours) |
Sixteen hours is enough to learn how to make sound design decisions. It isn’t enough to become hands-on with every framework. If your role needs both, take this course first and a bootcamp later.
Cost and dates
The current price is $450 in the US and ₹25,000 in India. Python for AI is listed as pre-work.
| Cohort | Dates |
|---|---|
| October | October 26 to 28, 2026 |
| November and December | November 30 to December 2, 2026 |
| December | December 28 to 30, 2026 |
Check the course page for current dates and time zones.
Additional discounts are available. Contact us to know more.
FAQ
Will I write code?
No. Python for AI is pre-work so you can read code and talk with engineers about it, but the course itself is design work.
Is the course tied to one cloud?
No. The platform decision framework compares providers, and the patterns apply across clouds.
Do I need generative AI experience?
No. You need architecture fundamentals and cloud familiarity. The course builds the AI side from there.
