Build AI Systems That Actually Work
Practical courses for people who want AI to become part of real work—structured workflows, bounded agents, controlled actions, testing, feedback, and repeatable systems instead of random prompts and vague hype.
Start with workflows. Move into agents. Then specialize in the tools, frameworks, controls, and operating practices that make AI useful in real work.
Choose what you want to build
Start with the kind of capability you need, then move deeper as your work becomes more technical, operational, or specialized.
Core AI systems
Build the durable foundation first: workflows, bounded agents, orchestration, and practical no-code implementation.
AI Workflow Course
Learn to turn repeated work into reliable AI workflows by defining inputs, context, rules, decision points, outputs, actions, tests, and improvement loops instead of relying on one-off prompts.
Explore learning path →AI Agent Course
Learn to design and build bounded AI agents around real goals, context, state, memory, tools, permissions, actions, verification, stop conditions, recovery, evaluation, observability, and human control.
Explore learning path →Agentic Workflows Course
Learn to orchestrate AI reasoning and tool-using agents inside larger controlled workflows that combine deterministic steps, state, routing, approvals, retries, handoffs, observability, and failure recovery.
Explore learning path →No-Code AI Agents Course
Learn to build useful bounded AI agents with visual and no-code tools while still applying serious system design: goals, context, tools, permissions, approvals, verification, failure recovery, evaluation, and maintenance.
Explore learning path →Teams & professional mastery
Operationalize AI-agent work across a team or demonstrate deeper applied mastery through performance-based certification.
AI Agent Training for Teams
Operationalize AI-agent work across a team with shared opportunity intake, design standards, permissions, evaluation, review gates, observability, incident handling, governance, and rollout practices.
Explore learning path →AI Agent Certification
Prove applied AI-agent design judgment through performance-based work covering problem framing, boundaries, context and memory, tools and permissions, reliability, evaluation, observability, governance, and an integrated capstone.
Explore learning path →Tools, protocols & frameworks
Go deeper into the technologies used to connect, structure, orchestrate, test, and deploy modern agent systems.
Model Context Protocol (MCP) Course
Learn the current Model Context Protocol as an implementation discipline: client/server roles, capabilities, tools and resources, authorization, transport, reliability, security, testing, deployment, and version-aware maintenance for agentic systems.
Explore learning path →AI Agent Frameworks Course
Learn how to evaluate and choose AI-agent frameworks based on architecture, state, tool use, orchestration, evaluation, deployment, observability, ecosystem maturity, and operational tradeoffs instead of chasing whichever framework is loudest this week.
Explore learning path →n8n AI Agents Workshop
Build a functioning AI-agent automation in n8n using triggers, data mapping, model calls, tools, sub-workflows, memory or state where appropriate, approvals, error handling, testing, and operational monitoring.
Explore learning path →PydanticAI Agents Workshop
Build practical Python AI agents with PydanticAI using typed dependencies and outputs, tools, model abstraction, validation, testing, evaluation, observability, and production-minded control patterns.
Explore learning path →Google ADK Agents Workshop
Build production-minded AI agents with Google Agent Development Kit by working through agent structure, tools, sessions/state, orchestration, evaluation, observability, deployment, and current ecosystem integrations.
Explore learning path →LangChain & LangGraph Agents Workshop
Build controlled AI-agent applications with current LangChain and LangGraph patterns for tools, state, graph execution, persistence, human intervention, testing, evaluation, observability, and production operations.
Explore learning path →Learn → build → test → improve
The course content matters, but the real product is learning how to turn AI into a system that survives contact with actual work.
Learn the system
Understand the pattern, boundaries, tradeoffs, and failure modes without drowning in abstract theory.
Build the design
Apply the idea to a realistic workflow or agent with explicit inputs, rules, tools, actions, and outcomes.
Test what fails
Use representative cases, verification, and failure analysis instead of treating one successful demo as proof.
Improve the system
Revise the design until it is clearer, safer, more reliable, more observable, and better suited to real use.