Practice agentic AI patterns. Like LeetCode, but for AI agents.
Free interactive games, two learning tracks, and a 3-minute AI career assessment.
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FreeDrag-and-drop agent building, architecture decisions, budget optimization. Leaderboard + scoring.
Try a gameTwo Tracks. Pick Yours.
For Developers
We teach you:
The 21 agentic design patterns — mapped to SWE concepts you already know. Code examples, architecture breakdowns, production notes.
For Product Managers
We give you:
Decision frameworks for agentic AI — zero code. The tradeoffs, questions, and vocabulary you need to lead AI product decisions.
Preview the curriculum
3 patterns + 3 PM modules shown. Sign up free to unlock everything.
For Developers — 3 of 21 patterns
Prompt Chaining
≈ Pipe & Filter
The foundational design pattern where a complex task is decomposed into a linear sequence of smaller, discrete LLM calls. The output of one step becomes the input (context) for the next step.
Routing
≈ Load Balancer / API Gateway
Dynamically directing a user request to the most appropriate specialized agent, model, or processing path based on the semantic intent and complexity of the query. A "Router" classifies the input and delegates execution to a downstream handler optimized for that specific task.
Parallelization
≈ Scatter-Gather / MapReduce
Executing multiple independent agentic tasks simultaneously — such as voting on a decision, generating multiple creative drafts, or verifying facts against different sources — and then aggregating the results. Also known as the "Sectioning" or "Voting" pattern.
For Product Managers — 3 of 15 modules
Becoming AI-Native
What agents and MCP actually are — and why every PM needs to understand them now
This is the foundation module. It answers the three questions every PM is quietly Googling: What exactly is an AI agent? What is MCP and why does everyone keep talking about it? And what does it mean to become AI-native as a product organization? The module establishes the vocabulary, mental models, and strategic lens that every subsequent module builds on.
AI Product Discovery
Knowing what to build before you build it
AI makes building fast. Cursor, Claude Code, and Replit Agent mean a prototype can exist in hours. This is revolutionary, but it creates a new risk: building the wrong thing faster. The PM's highest-value skill is no longer managing a backlog. It is identifying which problems are genuinely worth solving with AI, and which are just impressive demos that nobody needs. This module teaches the discovery frameworks specific to AI products.
The PM Prototyping Toolkit
From idea to working prototype in hours, not sprints
This is the module no other AI curriculum has. The core insight: tools like Cursor, Claude Code, Replit Agent, v0, Lovable, and Bolt have made it possible for non-engineers to build functional prototypes in hours. This does not make PMs into engineers. It collapses the feedback loop from weeks to hours. Instead of writing a PRD, waiting for a sprint, and hoping the team built what you imagined, you can show a working prototype in your next stakeholder meeting. The shift is from spec-then-build to build-then-learn.
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Take the Free AssessmentCommon Questions
Agentic AI refers to AI systems that autonomously perceive, reason, plan, and act to achieve goals. Unlike chatbots that respond to single prompts, agentic systems use LLMs as reasoning engines, access external tools, maintain memory, and execute multi-step workflows. There are 21 established design patterns for building these systems, each mapping to a classical software engineering concept. Learn Agentic Patterns (learnagenticpatterns.com) covers all 21 with code examples and interactive exercises.
Start with the 21 agentic design patterns — they map to concepts you already know. Prompt Chaining is Pipe & Filter. Reflection is TDD. Multi-Agent is Microservices. Tool Use is the Adapter Pattern. Learn the architecture first, then implement in any framework (LangChain, LangGraph, CrewAI, AutoGen). Building agents is software architecture, not prompt engineering. Learn Agentic Patterns (learnagenticpatterns.com) teaches all 21 patterns with SWE mappings, code examples, and interactive building exercises.
Software engineering is evolving, not dying. Senior developers already have 80% of the foundation: distributed systems, design patterns, production software. The gap is framing, not skill. Every agentic pattern has a SWE parallel. Learn the 21 agentic design patterns and you transition from building traditional systems to architecting intelligent autonomous systems.
No. The Developer Track is built for senior developers comfortable with distributed systems, APIs, and production software. The Product Manager Track is built for PMs who own or influence AI product decisions. Both tracks start from your existing knowledge — we don't teach coding basics or product management basics.
Yes. The PM Track has 10 decision-focused modules (zero code required) that reframe the 21 engineering patterns through a product lens. You'll learn tradeoff frameworks (cost vs. quality vs. latency), key product decisions for each pattern, questions to ask your engineering team, and practice with two interactive games: Ship or Skip (pick the right architecture for a scenario) and Budget Builder (allocate token budgets across model tiers).
No. The Product Manager track is entirely code-free. It explains what each agentic pattern does, why it matters for your product, what tradeoffs it introduces, and what questions you should be asking your engineering team. The interactive games test product judgment, not coding skill.
No. Patterns are framework-agnostic. We use pseudocode and real examples from LangChain, LangGraph, CrewAI, and AutoGen to illustrate, but the concepts apply universally. The PM track doesn't involve any framework at all.
Yes. Both the Developer and Product Manager tracks are completely free. 7 developer patterns are open without sign-up. Create a free account (no credit card) to unlock all 21 developer patterns, all 10 PM modules, and all interactive games.
LangChain teaches you how to use LangChain. Anthropic teaches you how to use Claude. DeepLearning.AI teaches AI fundamentals. This curriculum teaches the architecture layer between them — the 21 design patterns that determine which approach to use and why. It's framework-agnostic: once you understand why Prompt Chaining solves different problems than Routing or Parallelization, you can implement in any framework. Plus, 21 interactive exercises let you build and simulate agent architectures hands-on.
Antonio Gullí is an Engineering Leader at Google and author of 'Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems.' This curriculum is inspired by and builds upon his 21-pattern framework.