An Interactive Guide to Agentic Design Patterns
Agentic design patterns are reusable architectural blueprints for building sophisticated AI agents. These patterns enable agents to reason, plan, and autonomously use tools to solve complex problems. This guide provides an interactive exploration of the core concepts and popular patterns that power modern AI applications.
Anatomy of an AI Agent
Before diving into specific patterns, it's essential to understand the building blocks of any agentic system. This section provides an interactive breakdown of the key components that enable an agent to perceive, reason, and act. Click on each component to explore its role.
🧠 Planner
The core reasoning engine.
💾 Memory
Stores context and knowledge.
🛠️ Tools
External functions and APIs.
👤 Profile
Defines the agent's identity.
🧠 Planner
The Planner, often powered by a Large Language Model (LLM), is the agent's brain. It analyzes the user's goal, breaks it down into steps, decides which tools to use, and processes observations to determine the next action.
Key Design Patterns
Developers use several established patterns to structure agent behavior. These patterns provide frameworks for everything from simple tool use to complex, multi-agent collaboration. Explore some of the most common patterns below.
ReAct (Reason + Act)
The foundational pattern where an agent externalizes its reasoning process. It generates a "thought" explaining its logic, then an "action" to execute (like calling a tool). This makes the agent's behavior transparent and debuggable.
Reflection & Self-Correction
This pattern gives an agent the ability to review and critique its own work. After generating an initial response or plan, a "Critic" agent (or the same agent in a "critique" mode) evaluates the output against specific criteria and suggests improvements.
Draft
Output
Improve
Multi-Agent Collaboration
Complex tasks can be solved by a team of specialized agents. For example, a "Planner" agent creates a high-level plan, an "Executor" agent calls the tools, and a "QA" agent verifies the final result. This division of labor improves reliability.
Agent
Agent
Agents
Human-in-the-Loop
For critical or ambiguous tasks, it's vital to involve a human. This pattern defines points where the agent must pause and ask for human confirmation or input before proceeding. This ensures safety, alignment, and user control.
Proposes Plan
Approval
Executes
Pattern Comparison
Choosing the right pattern depends on your specific use case. This section provides a comparative view to help you decide. Use the buttons to toggle between comparing implementation complexity and level of autonomy.
Agent Simulator
See a simplified agent in action! Enter a goal and watch as the simulator uses the ReAct pattern to break down the problem and log its "thoughts" and "actions". This provides a concrete look at how an agent might process a request.
> Awaiting simulation start...