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.
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.
Draft
Output
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.
Agent
Agent
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.
Proposes Plan
Approval
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...