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.

Back to Tool Use

ApprenticeBuild Lab

Tool Use

Drag and drop agent blocks to build the correct architecture for this pattern.

Mission: Build a Data-Grounded Research Agent

Create an agent that can answer questions using real data by calling external tools (APIs, databases) instead of relying on its training data alone.

0.0s

> Drag blocks to the canvas

Query Planner

Decides which tools to call based on the question

API Adapter

Translates LLM requests into structured API calls

Database Adapter

Translates LLM requests into SQL queries

Synthesis Agent

Combines tool outputs into a coherent answer

Access Guardrail

Validates tool calls are within allowed scope

Task Splitter

Splits input into parallel subtasks

Drop blocks here to build your agent pipeline

Arrange them in the correct order