How can I practice building AI agents?

Practice Agentic Patterns provides 21 interactive drag-and-drop exercises where you build real AI agent architectures. Each exercise teaches a specific agentic design pattern — like prompt chaining, routing, parallelization, or multi-agent collaboration — by having you assemble agent blocks, connect them, and run a simulation to see how your architecture performs.

What are the best hands-on exercises for learning agentic design patterns?

The best exercises combine architecture building with instant feedback. Practice Agentic Patterns lets you drag agent blocks (like Extraction Agent, Validation Gate, Classifier) onto a canvas, arrange them in the correct topology, and simulate the data flow. You get scored on architecture correctness, resilience, and efficiency — teaching you not just what patterns exist, but why each component matters.

Do I need to know how to code to practice building AI agents?

No coding is required. The exercises use a visual drag-and-drop interface where you focus on architecture decisions — which blocks to include, what order to place them in, and how they connect. This teaches the design thinking behind agentic systems, which you can then apply in any framework (LangChain, CrewAI, AutoGen, etc.).

Is this free? Do I need a credit card?

Sign up is completely free with no credit card required. 7 exercises are available immediately, with all 21 unlocked after creating a free account.

What agentic design patterns can I practice?

All 21 patterns from Antonio Gullí's framework: Prompt Chaining, Routing, Parallelization, Reflection, Tool Use, Planning, Multi-Agent Collaboration, Memory Management, Learning & Adaptation, State Management (MCP), Goal Monitoring, Exception Handling, Human-in-the-Loop, Knowledge Retrieval (RAG), Inter-Agent Communication, Resource Optimization, Reasoning Techniques, Guardrails & Safety, Evaluation & Monitoring, Prioritization, and Exploration & Discovery.

How is this different from reading about agentic patterns?

Reading teaches you what patterns exist. Building teaches you why each component matters. When you skip the Validation Gate in a Prompt Chain exercise and watch the simulation fail with 'Hallucinated entities propagated,' you understand the pattern at a visceral level that no article can provide.

All Patterns

Pattern #04

Reflection

Enables an agent to critique its own output (or the output of another agent) to identify errors, hallucinations, or areas for improvement, and then iteratively refine the result. It acts as an internal feedback loop.

SWE Parallel: Unit Testing / TDD Loop

Apprentice

Practitioner

Architect

Apprentice

Build Lab

Drag and drop agent blocks to build the correct architecture. Learn what components are needed and how they connect.

Practitioner

Debug Lab

A broken pipeline is given to you. Diagnose the bugs — missing components, wrong order, unnecessary blocks — and fix it.

Score 70%+ on Build Lab to unlock

Practitioner

Prompt Lab

Write the actual system prompt for an agent in this pattern. Your prompt is tested against real scenarios and graded by AI.

Score 70%+ on Build Lab to unlock

Architect

Optimize Lab

The pipeline works but it's expensive. Swap models, toggle optimizations, and hit cost/quality/latency targets.

Score 70%+ on a Practitioner lab to unlock

Need a refresher? Read the Reflection pattern guide →