Agentic AI: Build & Deploy Autonomous Multi-Agent Systems for Business
Learn to design, build, and deploy production-ready autonomous AI agent workflows using CrewAI and LangGraph — so your business runs smarter, faster, and with less human bottleneck.
Perfect for: Technical business professionals, software developers, ML engineers, and tech-forward entrepreneurs who have basic Python familiarity and want to move beyond simple LLM prompting into building real autonomous systems. Also ideal for solutions architects and innovation leads tasked with identifying AI automation opportunities inside mid-to-large organizations.
The next competitive advantage isn't hiring more people — it's deploying agents that work for you 24/7.
Most businesses are still using AI like a fancy search engine: ask a question, get an answer, move on. But the companies pulling ahead are doing something fundamentally different — they're building networks of autonomous AI agents that plan, delegate, execute, and self-correct entire workflows without a human in the loop. This school teaches you exactly how to do that.
From concept to production — no PhD required.
We cut through the academic noise and focus on what actually works in a business context. You'll get hands-on with the two frameworks that matter most right now: CrewAI for role-based multi-agent orchestration, and LangGraph for building stateful, graph-driven agent pipelines. Whether you're automating a research workflow, a customer support pipeline, or an internal operations process, you'll know how to architect it, build it, and ship it — not just demo it.
Built for builders who have real work to do.
This isn't a survey course of AI buzzwords. Every concept is anchored to a realistic business use case — lead enrichment agents, document analysis pipelines, autonomous reporting crews, and more. You'll leave with reusable agent templates, a decision framework for choosing the right architecture, and the confidence to scope and pitch agentic solutions to stakeholders inside your own organization.
Why now?
The tooling for agentic AI matured rapidly in 2024 and is stabilizing in 2025. The window to get ahead of your industry is open — but it won't stay open forever. Teams that understand how to deploy reliable, observable, cost-controlled agent systems today will be the ones setting the standard everyone else copies tomorrow.
What you'll be able to do
- Explain the core architecture of multi-agent systems — agents, tools, memory, and orchestration — and when to use agentic workflows vs. simpler LLM calls.
- Set up and configure production-grade multi-agent pipelines using CrewAI, including defining agent roles, goals, and inter-agent task delegation.
- Build stateful, graph-driven agentic workflows in LangGraph with conditional branching, human-in-the-loop checkpoints, and error recovery.
- Design and integrate custom tools (APIs, databases, web scrapers, code executors) that agents can autonomously invoke to complete real-world tasks.
- Implement observability and evaluation practices — including logging, tracing with LangSmith, and cost monitoring — so your agents are debuggable and trustworthy in production.
- Apply a repeatable architecture decision framework to assess any business process and determine the right agent topology: single agent, sequential crew, hierarchical crew, or graph-based pipeline.
- Deploy multi-agent systems to cloud infrastructure with appropriate guardrails, rate limiting, and human escalation paths to meet real business reliability standards.
- Scope, pitch, and document an agentic automation project for internal stakeholders, including ROI framing and risk considerations.
Curriculum
6 modules · 20 lessons
Your teacher
Sherrie K Licon
I've spent the last several years at the intersection of software engineering and applied AI — building systems that actually run in production, not just in notebooks. When agentic frameworks started maturing, I went deep: tearing apart CrewAI and LangGraph internals, running multi-agent systems on real business workflows, and learning the hard lessons about what breaks at scale (and why). I built this school because most of the content out there stops at the "cool demo" stage. My goal is to get you past that — into architectures you can defend, deploy, and maintain. If you're ready to build AI systems that genuinely work autonomously, I'll show you exactly how I do it.
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