Build reliable, secure, and safe AI systems
Move beyond demos and notebooks. Learn how to test, secure, deploy, and operate agentic AI systems that are ready for real users, real data, and real-world risk.
How agentic AI systems survive the real world
Build and operate real systems — earn a credential if you choose
Production agentic AI is becoming a core engineering responsibility
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This program centers on hardening an agentic AI system for real-world use.
You'll add testing, safety, deployment, and monitoring to an existing system.
The focus is on reliability, maintainability, and long-term operation.
You'll get full access to lessons, walkthroughs, and production-style codebases.
The program culminates in one capstone project: a production-ready agentic AI system.
For those who want formal recognition, earning the Agentic AI Engineer credential requires:
Revisions and re-submissions are allowed based on expert feedback from our team.
Learn and showcase the AI skills top employers are hiring for
Custom RAG Workflow
LangGraph RAG Assistant
A LangGraph-powered assistant that answers complex questions using real documentation. Built with Qdrant, and ReAct-based reasoning.
Knowledge RAG Assistant
RAG System with FAISS
A production-ready RAG assistant built with LangChain and FAISS to answer questions from a structured JSON knowledge base.
Multi-Agent Research System
Cerebral Collective
A multi-agent research assistant with human oversight, built using FastAPI and local LLM inference.
Multi-Agent RAG Assistant
Lumigo Academic Q&A
A modular Q&A system for academic documents with transparent citations, fallback thesis search, and multi-agent orchestration.
Build systems that hold up under real-world use
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Payable in your local currency at checkout.
Real experience operating agentic AI systems
Answers to common questions about the Agentic AI in Production program.
This program is for developers who want to take agentic AI systems from prototype to production, with a focus on reliability, safety, deployment, and long-term operation. It's ideal for software developers, data scientists, and ML engineers preparing for AI Engineer or LLM Engineer roles.
You can, but this program assumes you already understand core agent concepts such as prompts, tools, memory, and workflows. If you're completely new to agentic AI, many learners start with an introductory or multi-agent program first.
You should be comfortable with intermediate Python, including functions, classes, error handling, and working with APIs and libraries. Familiarity with basic backend or web concepts is helpful.
You don't need a formal background in machine learning, deep learning, or data science. Comfort with basic programming concepts and working with APIs is enough to succeed.
Most learners complete the program in about 3-4 weeks. The program is fully self-paced, and the timeline depends on your prior experience and the scope of your production project.
You'll need an LLM API key. Some deployment or monitoring steps may involve minimal cloud usage, but most learners stay within free or low-cost tiers.
No. You may either productionize an existing system or build a new one during the program, as long as it meets the production readiness requirements.
You'll receive detailed feedback and can revise and resubmit your project in a future review cycle. Iteration is expected for production-grade systems.
The program gives you the option to earn the Agentic AI Engineer credential after successful review of your production project. Regardless of certification, you'll finish with a publicly published, production-ready system demonstrating real-world operational skills.
This program prepares you for AI Engineer and ML Engineer roles focused on deploying and operating agentic AI systems. Typical projects include production RAG systems, AI-powered services, internal automation tools, and monitored, safety-aware AI applications.
Build production-grade Agentic AI systems you can operate with confidence.
Limited-time discounted price: {{pricing.price}}
Payable in your local currency at checkout.