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.

Pattern Labs

21 Agentic Design Patterns

Each pattern has up to 4 challenge types across 3 difficulty tiers. Build architectures, debug broken pipelines, write prompts, and optimize costs.

#01

Prompt Chaining

The foundational design pattern where a complex task is decomposed into a linear sequence of smaller...

4 labs

#02

Routing

Dynamically directing a user request to the most appropriate specialized agent, model, or processing...

4 labs

#03

Parallelization

Executing multiple independent agentic tasks simultaneously — such as voting on a decision, generati...

4 labs

#04

Reflection

Enables an agent to critique its own output (or the output of another agent) to identify errors, hal...

4 labs

#05

Tool Use

Extends the capabilities of an LLM by allowing it to interact with the external world (APIs, databas...

4 labs

#06

Planning

The capability of an agent to break down a high-level, ambiguous user goal into a sequence of execut...

4 labs

#07

Multi-Agent Collaboration

Structuring a system as a team of specialized agents (e.g., a Researcher, a Writer, a Reviewer, a Co...

4 labs

#08

Memory Management

Mechanisms for agents to store, index, and retrieve information over time, spanning short-term (conv...

2 labs

#09

Learning & Adaptation

The ability of an agent to improve its performance over time based on feedback, user interactions, o...

2 labs

#10

State Management (MCP)

A standardized protocol (Model Context Protocol) for connecting AI models to data sources and tools,...

2 labs

#11

Goal Setting & Monitoring

Mechanisms for agents to define high-level objectives, break them into trackable sub-goals, and cont...

2 labs

#12

Exception Handling & Recovery

Strategies for agents to detect failures (e.g., API errors, hallucinations, logic loops) and self-co...

2 labs

#13

Human-in-the-Loop

Integrating human oversight at critical decision points to validate agent actions, provide feedback,...

2 labs

#14

Knowledge Retrieval (RAG)

Retrieval-Augmented Generation (RAG) equips agents with the ability to query external knowledge base...

2 labs

#15

Inter-Agent Communication (A2A)

Protocols and formats for autonomous agents to exchange messages, tasks, and state information to co...

2 labs

#16

Resource-Aware Optimization

Agents aware of their token consumption, API costs, and computational limits, optimizing their strat...

2 labs

#17

Reasoning Techniques

Advanced cognitive architectures like Chain-of-Thought (CoT), Tree-of-Thought (ToT), and ReAct (Reas...

2 labs

#18

Guardrails & Safety

Architectural safeguards (input/output filters) to prevent agents from executing harmful actions, le...

2 labs

#19

Evaluation & Monitoring

Frameworks for measuring agent performance (accuracy, faithfulness, tool usage) and monitoring behav...

2 labs

#20

Prioritization

The ability of an agent to rank disparate tasks or goals based on urgency, importance, and constrain...

2 labs

#21

Exploration & Discovery

Agents proactively seeking new information, testing hypotheses, or exploring an environment to expan...

2 labs