Welcome to Agentic AI in Production.
If you’re here, you already know how to build agentic systems. Now comes the harder — and more important — question:
Can this system survive the real world?
This program is about what happens after the demo works.
Instead of asking:
You’ll start asking:
This is where agentic AI stops being experimental and starts becoming engineering.
It’s tempting to treat educational programs as a checklist of lessons to get through. That’s not how this program works — and it’s not how you earn certification.
This is a project-based certification program. You don’t earn certification for reading articles or watching videos about safety, testing, or deployment. You earn it by turning an agentic system into something that can be trusted, evaluated, and operated.
Your primary focus should be clear:
Take an existing agentic system and make it production-ready.
Think of this the way real AI engineering teams operate. The project defines the goal. The lessons, videos, and tools exist to help you reach it — they are means, not milestones.
In this program, that goal is to transform an agentic AI system into a deployable application with:
If these topics feel unfamiliar or intimidating, that’s expected.
Production readiness is where most AI projects fail — and exactly why this program exists.
As you work, keep these priorities in mind:
You don’t need to build a massive enterprise-grade system with 30 agents and a million lines of code. You need to build a practical one that holds up under real-world conditions and reflects sound engineering judgment.
That mindset — treating reliability and safety as first-class concerns — is what will help you succeed in this program.
You don’t need to “prepare” before you begin. The best way to start is to start.
Here’s a simple path that works well for most learners:
First, read the project description so you understand what “production-ready” means in this context.
Then, decide whether you’ll work solo or with a team — both are fully supported.
Next, skim the curriculum to see what lessons and resources are available, not to memorize them, but to know where to look when you need guidance.
Finally, begin hardening the system early and improve it iteratively.
You’ll learn faster by testing, breaking, and fixing the system than by reading everything upfront.
Production problems are rarely obvious — and rarely solvable alone.
Whether you’re stuck on testing strategy, unsure how to monitor agent behavior, or debating deployment tradeoffs, the community is a powerful resource.
In the Discord community, you can:
Engineering is a team sport. Use the team.
When you’re ready, head to the Project Curriculum for Agentic AI Engineer.
That page outlines the lessons and resources that support turning an agentic system into a production-ready application.
Start there — then start hardening your system.
This is where good ideas become reliable systems.
Let’s engineer something you can trust.
