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.
Memory
Stores conversation history and learned knowledge for context.
Tools
APIs and functions the agent can use, like web search or calculators.
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.
Reflection
The agent critiques and improves its own work, leading to higher quality results.
Multi-Agent
A team of specialized agents collaborates to solve complex problems.
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.