Build AI Agents: From Zero to Production

AI agents are the next frontier. Learn to build agents that can reason, plan, and execute complex tasks.

What Are AI Agents?

AI agents are systems that:

• Understand goals
• Plan steps to achieve them
• Execute actions
• Learn from results
• Adapt their approach

Architecture Components

1. Planning Module

Breaks down complex tasks:

# Example: Trip planning agent
goal = "Plan a 7-day trip to Japan"
steps = [
  "Research destinations",
  "Check flight prices",
  "Find hotels",
  "Create itinerary",
  "Book reservations"
]

2. Memory System

Types of memory:

• Short-term: Current conversation
• Long-term: Past interactions
• Vector: Semantic search

3. Tool Use

Common tools:

• Web search
• API calls
• Code execution
• Database queries

Building with LangChain

Basic Agent

from langchain.agents import initialize_agent
from langchain.tools import Tool

# Define tools
tools = [
  Tool(name="Search", func=search_google),
  Tool(name="Calculator", func=calculate),
]

# Initialize agent
agent = initialize_agent(
  tools,
  llm,
  agent="zero-shot-react-description",
  verbose=True
)

# Run
agent.run("Find the top 3 AI companies and their funding")

Advanced Patterns

Multi-Agent Systems

Multiple agents collaborating:

• Researcher agent
• Writer agent
• Editor agent
• Publisher agent

ReAct Pattern

Reasoning + Acting:

1. Thought: What should I do?
2. Action: Execute tool
3. Observation: Review result
4. Repeat until done

Production Considerations

Monitoring

• Track agent decisions
• Log tool usage
• Measure success rates
• Alert on failures

Safety

• Sandbox code execution
• Rate limit API calls
• Human approval gates
• Output validation

Use Cases

Customer Support Agent

• Understands tickets
• Searches knowledge base
• Escalates when needed
• Learns from feedback

Research Assistant

• Searches multiple sources
• Synthesizes information
• Creates summaries
• Cites sources

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Start simple. Build one agent for one task. Scale from there.