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.
What you'll learn
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.
How it works
A school that adapts to you
This isn't a set of static videos. Every lesson is generated live and tuned to where you actually are.
We learn your level
A quick placement check tailors your starting point so you're never bored or lost.
Lessons adapt as you go
Each lesson is written for your pace and your goal, adjusting as your skills grow.
Your AI coach keeps you moving
Checkpoints, feedback, and gentle nudges turn progress into a real result.
The curriculum
What's inside your school
6 modules · 20 lessons
Foundations of Agentic AI — Mental Models & Architecture
Establish a rock-solid conceptual foundation before writing a single line of agent code. Students learn what makes a system truly 'agentic,' how agents differ from simple LLM chains, and how to recognize which business problems are worth solving with autonomous workflows. This module prevents the #1 mistake practitioners make: reaching for agents when a prompt would do.
- 1.1What Is an AI Agent? (And What It Isn't)Included
- 1.2Memory, Tools, and Orchestration — The Three PillarsIncluded
- 1.3The Agent Decision Framework — When to Use WhatIncluded
Building Multi-Agent Crews with CrewAI
Get hands-on with CrewAI — the most business-accessible multi-agent framework. Students progress from configuring their first agent to running a fully functional, role-specialized crew that completes a real business workflow. Every lesson ships working code by the end of the session.
- 2.1CrewAI Fundamentals — Agents, Tasks, and Your First CrewIncluded
- 2.2Role Specialization & Inter-Agent Task DelegationIncluded
- 2.3Custom Tools — Connecting Agents to Real Business SystemsIncluded
- 2.4Production-Ready CrewAI — Configuration, Error Handling & Cost ControlIncluded
Stateful & Conditional Workflows with LangGraph
Graduate to LangGraph for workflows that require fine-grained control: conditional branching, loops, human-in-the-loop interrupts, and complex state management. Students learn when LangGraph is the right choice over CrewAI (and vice versa) and build graph-driven pipelines that mirror real business approval and escalation processes.
- 3.1LangGraph Core Concepts — Graphs, Nodes, Edges, and StateIncluded
- 3.2Conditional Branching, Loops & Error RecoveryIncluded
- 3.3Human-in-the-Loop — Checkpoints, Interrupts & Approval GatesIncluded
- 3.4Stateful Memory & Long-Running WorkflowsIncluded
Observability, Evaluation & Trust in Production
An agent that can't be observed can't be trusted — and can't be improved. This module covers the full observability stack: structured logging, LangSmith tracing, cost dashboards, and systematic evaluation. Students build the monitoring infrastructure that separates hobbyist experiments from systems a business can actually rely on.
- 4.1Tracing & Debugging with LangSmithIncluded
- 4.2Evaluation Frameworks — Measuring Agent QualityIncluded
- 4.3Cost Monitoring, Rate Limiting & Reliability GuardrailsIncluded
Cloud Deployment, Security & Scalable Infrastructure
Bridge the gap between a working local prototype and a system that runs reliably in production. Students containerize their agents, deploy to cloud infrastructure, implement authentication and secret management, configure autoscaling, and set up alerting. By the end, students have a live deployment URL they can actually share with stakeholders.
- 5.1Containerizing Agents — Docker & Environment ParityIncluded
- 5.2Cloud Deployment — Serving Agents as APIsIncluded
- 5.3Guardrails, Human Escalation Paths & Incident ResponseIncluded
Capstone — Scope, Build & Pitch a Real Business Agent
Synthesize everything. Students identify a real automation opportunity in their own organization (or a provided case study), apply the architecture decision framework, build a working prototype, instrument it for production, and deliver a stakeholder pitch complete with ROI framing, risk register, and deployment roadmap. This is the artifact students take back to their organizations.
- 6.1Opportunity Assessment & Architecture BlueprintIncluded
- 6.2Prototype Build SprintIncluded
- 6.3Stakeholder Pitch & Deployment RoadmapIncluded
Questions
Frequently asked
Your teacher
A note from your teacher
ParlioVox Learning
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.
— ParlioVox Learning
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- 6 modules, 20 lessons
- AI-adaptive lessons tuned to your level
- Quizzes & checkpoints to lock in progress
- Your own AI learning coach
- Learn on any device, at your pace
- Full access for as long as you're subscribed