Anatomy of an Agent

Learn the core components that make up an AI agent and how they work together

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Introduction

Core Concepts

Interactive Demo

Practical Application

Key Takeaways

Introduction

An AI agent isn't magic—it's an engineered system with distinct, interlocking components. Understanding agent anatomy helps you build better systems, debug failures faster, and design for production.

What's Inside an Agent?

Think of an agent like a self-driving car: it has sensors (inputs), a brain (reasoning), arms (tools), and memory (context). Every modern agent—from ChatGPT plugins to AutoGPT—shares these core components.

🧠

Reasoning Engine

The "brain" powered by an LLM. Analyzes tasks, plans actions, and decides what to do next.

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Memory Systems

Short-term (conversation context), long-term (vector databases), and episodic (past experiences).

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Tool Interface

APIs, databases, code executors—anything the agent can "use" to interact with the world.

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Control Loop

The execution pattern (ReAct, Plan-Execute, Reflexion) that orchestrates everything.

Interactive: Explore Agent Components

Click on each component to see its role in the agent system:

🧠 Reasoning Engine

The core LLM that powers decision-making. Receives inputs, generates thoughts, and produces structured outputs.

Input: "Book a flight to Paris"

Thought: "I need to search for available flights, compare prices, and select the best option"

Output: {action: "search_flights", params: {destination: "Paris"}}

✓ Powered by GPT-4, Claude, or similar LLMs
✓ Uses prompts to guide reasoning patterns (ReAct, CoT)
✓ Can be fine-tuned for specialized domains

Why Agent Anatomy Matters

🔍Debug Failures Faster

When an agent fails, knowing the anatomy helps you pinpoint the issue: Was it a reasoning error? Tool misconfiguration? Memory retrieval failure?

🏗️Design Better Systems

Understanding components helps you make architectural decisions: Which memory system to use? How many tools to expose? What control loop fits your use case?

⚡Optimize Performance

Each component has performance implications: Context window size affects latency, tool calls add overhead, memory retrieval impacts accuracy.

🎛️Customize Behavior

Want a more cautious agent? Adjust the control loop. Need domain expertise? Fine-tune the reasoning engine. Each component is a lever you can pull.

🎯What You'll Learn

→Core Concepts: Deep dive into each component—how they work, trade-offs, and design patterns
→Interactive Demo: Build your own agent by configuring components and seeing them work together
→Practical Application: Real-world examples from production systems (ChatGPT, GitHub Copilot, Intercom Fin)
→Key Takeaways: Decision framework for choosing components and architectural patterns