🤖 AI Agents

Build intelligent autonomous systems that reason, plan, and execute

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Welcome to the AI Agents section — your comprehensive guide to building intelligent systems that can perceive, decide, and act. This isn’t about chatbots. This is about autonomous agents that accomplish real work.

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Cookbook as Code

PythonLangChainOpenAIAnthropicAgent Frameworks

This cookbook includes runnable code examples. Fork, experiment, and build!

View on GitHub🍴Fork & Run📋Copy URL

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🎯 Learning Path

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AI Agent Developer Journey

From understanding agent fundamentals to building production-ready autonomous systems

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1

Agent Development Kits (ADK)

🌿 intermediate ⏱️ 40 min

Understand the toolkit fundamentals and frameworks that power modern agent development — LangChain, AutoGPT, CrewAI, and more

Start Learning ✓ Mark Complete

2

Agent Architectures & Types

🌿 intermediate ⏱️ 50 min

Master Sequential, Parallel, Loop, and Custom agent patterns — learn when to use each architecture

Prerequisites: ADK fundamentals

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3

Agent Models & Contracts

🌳 advanced ⏱️ 45 min

Design robust agent APIs and communication protocols — the contracts that enable multi-agent systems

Prerequisites: Agent types

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4

Defining LLM Agents

🌳 advanced ⏱️ 60 min

Step-by-step implementation guide — turn concepts into working code with tool use, memory, and reasoning

Prerequisites: Agent models

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5

Tool Ecosystems & MCP

🌳 advanced ⏱️ 50 min

Connect agents to the real world — APIs, databases, browsers, and the Model Context Protocol

Prerequisites: Defining agents

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6

Multi-Agent Orchestration

🌳 advanced ⏱️ 55 min

Build systems where multiple agents collaborate — delegation, communication, and collective intelligence

Prerequisites: Tool ecosystems

Start Learning ✓ Mark Complete

🔄 Reset Progress

🎉 Congratulations! You've completed this learning path!

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🔧 What You’ll Build

🛠️ Foundation: Agent Development Kits (ADK)

Before building agents, understand the ecosystem:

• LangChain — The Swiss Army knife of agent development
• LlamaIndex — Data-centric agent architecture
• AutoGPT/BabyAGI — Autonomous agent pioneers
• CrewAI — Multi-agent role-based systems
• Semantic Kernel — Microsoft’s enterprise agent framework
• Custom Implementations — When frameworks aren’t enough

→ Start with ADK Overview

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🔄 Agent Types & Architectures

Different problems require different agent architectures:

Architecture	Pattern	Best For
Sequential	A → B → C	Linear workflows, pipelines
Parallel	A + B + C	Independent subtasks, speed
Loop	A → B → A…	Iteration, refinement
Hierarchical	Manager → Workers	Complex delegation
Reactive	Event → Response	Real-time systems
Hybrid	Custom composition	Complex workflows

→ Master Agent Types

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📋 Agent Models & Contracts

Production agents need clear interfaces:

# Example Agent Contract
class AgentContract:
    """Define what an agent can do and how to interact with it."""

    name: str                    # Unique identifier
    description: str             # What this agent does
    input_schema: Schema         # Expected input format
    output_schema: Schema        # Guaranteed output format
    tools: list[Tool]            # Available capabilities
    max_iterations: int          # Safety limit
    timeout_seconds: float       # Maximum execution time

→ Design Agent Contracts

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🧬 Defining LLM Agents

The core of agent development — from concept to code:

# Simplified Agent Definition
from langchain.agents import Agent, Tool

researcher = Agent(
    name="Researcher",
    goal="Find accurate information on any topic",
    tools=[
        Tool(name="search", func=web_search),
        Tool(name="read", func=read_webpage),
        Tool(name="summarize", func=summarize_text)
    ],
    llm=ChatOpenAI(model="gpt-4"),
    memory=ConversationBufferMemory()
)

# The agent can now autonomously research any topic
result = researcher.run("What are the latest advances in AI agents?")

→ Build Your First Agent

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💡 Key Insights

The Agent Mindset Shift

Agents aren’t chatbots with extra steps. They’re autonomous systems that plan, execute, observe, and adapt. The key shift is from “prompt engineering” to “goal engineering” — you define objectives, not steps.

Start Simple, Scale Carefully

The most common mistake is over-engineering. Start with a simple sequential agent. Add complexity only when you’ve validated the simple version works. Multi-agent systems are powerful but dramatically harder to debug.

Cost Awareness

Agents can run up significant LLM costs through iteration loops. Always implement:

• Maximum iteration limits
• Timeout boundaries
• Cost monitoring
• Graceful degradation

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🌱 → 🌿 → 🌳 Progressive Learning

Each guide follows our growth taxonomy:

Level	Symbol	Focus
Seedling	🌱	Core concepts explained simply
Sprout	🌿	Practical implementation with examples
Forest	🌳	Production-ready patterns
Insight	💡	Critical understanding points
Quick Win	⚡	Ready-to-use code snippets
Deep Dive	🔬	Advanced research topics

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⚡ Quick Wins

Get started immediately with these copy-paste examples:

Minimal Research Agent

from openai import OpenAI

client = OpenAI()

def simple_agent(task: str) -> str:
    """Minimal viable agent using GPT-4."""
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": """You are a research agent.
            For any task:
            1. Break it into steps
            2. Think through each step
            3. Provide a comprehensive answer"""},
            {"role": "user", "content": task}
        ]
    )
    return response.choices[0].message.content

# Usage
result = simple_agent("Explain the key differences between LangChain and LlamaIndex")

Tool-Using Agent (LangChain)

from langchain.agents import initialize_agent, Tool
from langchain.llms import OpenAI
from langchain.tools import DuckDuckGoSearchRun

# Define tools
search = DuckDuckGoSearchRun()
tools = [
    Tool(
        name="Search",
        func=search.run,
        description="Useful for searching the web for current information"
    )
]

# Create agent
agent = initialize_agent(
    tools,
    OpenAI(temperature=0),
    agent="zero-shot-react-description",
    verbose=True
)

# Run agent
agent.run("What is the current price of Bitcoin?")

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🎓 Who This Is For

💻 Developers

• Python or TypeScript experience
• Familiar with APIs and async programming
• Want to build AI-powered applications
• Ready for production challenges

🏗️ Architects

• Designing AI systems at scale
• Multi-agent orchestration needs
• Enterprise integration requirements
• Performance and cost optimization

🔬 Researchers

• Exploring agent capabilities
• Experimenting with architectures
• Pushing boundaries of what’s possible
• Publishing and sharing findings

🚀 Builders

• Startup founders with AI products
• SaaS developers adding AI features
• Automation engineers
• Anyone shipping AI to production

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📋 Prerequisites

To get the most from this section:

• ✓ Python 3.10+ — Primary language for examples
• ✓ API Fundamentals — REST, async/await, error handling
• ✓ LLM Basics — Understanding of prompts and completions
• ✓ Git — Clone and run example repositories
• ✓ OpenAI API Key (or equivalent) — For running examples

Recommended but not required:

• Docker for containerized deployments
• Basic understanding of vector databases
• Familiarity with one agent framework

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🚀 Real-World Applications

Agents you’ll learn to build:

Agent Type	Example Use Case	Complexity
Research Agent	Automated market analysis	🌱 Beginner
Writing Agent	Content generation pipeline	🌱 Beginner
Code Agent	Automated PR reviews	🌿 Intermediate
Data Agent	ETL and analysis automation	🌿 Intermediate
Customer Service	Intelligent ticket routing	🌳 Advanced
Multi-Agent System	Full business process automation	🌳 Advanced

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🔗 Related Sections

After mastering Agent basics:

• Tools & MCP — Extend agent capabilities
• RAG — Give agents knowledge bases
• Advanced Prompting — Optimize agent instructions
• Legendary — Production deployment at scale

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❓ Frequently Asked Questions

Do I need expensive GPT-4 for agents?

Not necessarily. Start with GPT-3.5-turbo for development and testing (10x cheaper). Use GPT-4 for production where reasoning quality matters. For cost-sensitive applications, explore open-source models via Ollama or together.ai.

How do I prevent agents from running forever?

Implement three safeguards: (1) Maximum iteration count, (2) Timeout limits, (3) Cost caps. Most frameworks support these out of the box. Our guides cover implementation in detail.

Can agents access the internet safely?

Yes, with proper sandboxing. Never give agents unrestricted system access. Use tool abstractions that limit scope. Our Tool Ecosystems guide covers safe patterns for web access.

What’s the difference between agents and chains?

Chains are predetermined sequences (A→B→C). Agents decide their own actions based on goals and observations. Chains are predictable but inflexible. Agents are adaptive but harder to control. Use chains when you know the exact workflow; use agents when you know the goal but not the path.

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Part of the HUB Cookbooks by CURATIONS — A Human × AI Creative Agency

← Back to All Cookbooks | Start with ADK Overview →