Agentic Design Patterns for AI Agents

Explore agentic design patterns to create advanced AI agents that think, strategize, and tackle complex challenges. Interactive guide with examples.


Agentic Design Patterns Explorer

An Interactive Guide to Agentic Design Patterns

Agentic design patterns serve as versatile templates for crafting advanced AI agents. They empower agents to think, strategize, and independently utilize tools to tackle intricate challenges. This resource offers a hands-on journey through key principles and widely used patterns driving today’s AI innovations.

Anatomy of an AI Agent

Before exploring specific patterns, it's crucial to grasp the core elements of any agentic system. This part offers a hands-on overview of the main components that allow an agent to sense, think, and act. Tap each element to delve into its function.

🧠 Planner

The core reasoning engine.

💾 Memory

Stores context and knowledge.

🛠️ Tools

External functions and APIs.

👤 Profile

Defines the agent's identity.

🧠 Planner

The Planner, typically driven by a Large Language Model (LLM), acts as the agent's core. It interprets the user's objective, segments it into tasks, selects tools, and evaluates feedback to decide the next move.

Key Design Patterns

Developers rely on proven patterns to design agent behavior, offering frameworks for tasks ranging from basic tool usage to intricate multi-agent teamwork. Discover key patterns below.

ReAct (Reason + Act)

A core framework where an agent articulates its reasoning. It produces a 'thought' to outline its logic, followed by an 'action' to perform (e.g., using a tool). This ensures clarity and facilitates debugging.

Thought: I need to find the weather in Paris.
Action: `search_weather(city="Paris")`
Observation: The weather is 15°C and sunny.
Thought: I have found the weather. The task is complete.

Reflection & Self-Correction

This approach enables an agent to analyze and refine its own output. By producing an initial response or plan, a 'Critic' agent (or the same agent in a critical mode) assesses the result using set criteria and recommends enhancements.

Generate
Draft
Critique
Output
Refine &
Improve

Multi-Agent Collaboration

Complex problems are tackled by teams of specialized agents: a 'Planner' drafts the strategy, an 'Executor' uses tools, and a 'QA' ensures accuracy. This structured approach boosts reliability.

Manager
Agent
Planner
Agent
Executor
Agents

Human-in-the-Loop

For complex or uncertain tasks, involving a human is essential. This approach highlights moments where the agent should stop and seek human input or approval, ensuring alignment, safety, and user oversight.

Agent
Proposes Plan
⚠️ Human
Approval
Agent
Executes

Pattern Comparison

Selecting the ideal pattern hinges on your unique needs. This guide offers a side-by-side comparison to assist your choice. Toggle the buttons to switch between assessing complexity and autonomy levels.

Agent Simulator

Watch an agent in action! Set a goal and see the simulator apply the ReAct pattern to tackle the task, logging its 'thoughts' and 'steps.' Get a clear view of how an agent approaches problem-solving.

> Awaiting simulation start...


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