The Blueprint for Smart AI

Agentic AI can reason, plan, and use tools to solve problems. This infographic breaks down the essential design patterns that make these advanced AI systems possible.

Anatomy of an AI Agent

An agent is more than just a model. It's a system of components working together. The Large Language Model (LLM) acts as the core reasoning engine, or "Planner," coordinating with other key parts.

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Memory

Stores conversation history and learned knowledge for context.

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Tools

APIs and functions the agent can use, like web search or calculators.

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Profile

Instructions defining the agent's identity, goals, and constraints.

Core Design Patterns

Patterns are reusable strategies for structuring how an agent thinks and acts. These are the fundamental blueprints for building capable agents.

ReAct

The agent "thinks out loud" before acting, making its reasoning transparent.

Thought Action Observe

Reflection

The agent critiques and improves its own work, leading to higher quality results.

Generate Critique Refine

Multi-Agent

A team of specialized agents collaborates to solve complex problems.

Manager Specialists

How to Choose a Pattern

The right pattern depends on your goal. This chart compares key patterns across different factors to help you decide which blueprint fits your project's needs.

ReAct Pattern In Action

Here’s a simplified look at how an agent uses the ReAct pattern to answer a question. Notice the cycle of thinking, acting, and observing the result.

USER GOAL

"What's the weather in Paris?"

THOUGHT

I need to find the weather. I should use my weather search tool.

ACTION

search_weather(city="Paris")

OBSERVATION

API returned: { "temp": "15°C", "condition": "Sunny" }

THOUGHT

I have the answer. I will now respond to the user.

FINAL ANSWER

The weather in Paris is 15°C and sunny.