AI Agents: From Concepts to Production

import { Card, CardGrid, LinkCard, Aside, Steps } from ‘@astrojs/starlight/components’;

AI agents are autonomous systems that can perceive their environment, make decisions, and take actions to achieve specific goals. Unlike simple chatbots, agents can use tools, plan multi-step tasks, and adapt their behavior based on feedback.

What are AI Agents?

AI agents combine language models with the ability to:

• Perceive: Understand user requests and environmental context
• Plan: Break down complex tasks into actionable steps
• Act: Use tools and APIs to accomplish goals
• Learn: Adapt based on feedback and results

Agents can automate complex workflows that require: - Multiple tool interactions - Decision-making at each step - Error handling and retry logic - Contextual awareness - Dynamic planning

Examples: Code generation tools, research assistants, customer support automation, data analysis pipelines.

Learning Path

Follow these guides to build powerful AI agents:

**Level**: Intermediate **Time**: 45-60 minutes

Start here to understand the fundamental patterns and architectures for building AI agents. Learn about different agent types, design patterns, and when to use each approach.

**What you'll learn**:
- ReAct (Reasoning + Acting) pattern
- Plan-and-Execute agents
- Autonomous agents
- Multi-agent systems
- Agent orchestration
- Error handling patterns

<LinkCard
  href="/developers/ai-agents/agent-architecture-patterns"
  title="Learn Agent Patterns →"
  description="Understand agent architectures and design patterns"
/>

**Level**: Intermediate to Advanced **Time**: 60-75 minutes

Build agents that can interact with external tools and APIs using function calling. Learn how to create tool definitions, handle tool execution, and chain multiple function calls.

**What you'll learn**:
- OpenAI function calling
- Tool/function schemas
- Dynamic tool selection
- Error handling and validation
- Chaining multiple tools
- Building custom tools

<LinkCard
  href="/developers/ai-agents/function-calling-agents"
  title="Build Function Agents →"
  description="Create agents that use tools and APIs"
/>

**Level**: Intermediate to Advanced **Time**: 50-65 minutes

Compare and implement agents using popular frameworks like LangChain, LlamaIndex, AutoGPT, and others. Learn the trade-offs and when to use each framework.

**What you'll learn**:
- LangChain agents
- LlamaIndex agents
- AutoGen multi-agent systems
- CrewAI for task delegation
- Framework comparison
- Migration strategies

<LinkCard
  href="/developers/ai-agents/llm-agent-frameworks"
  title="Master Frameworks →"
  description="Compare agent frameworks and choose the right one"
/>

Quick Start Roadmap

1. Understand the patterns (1-2 days)

   Begin with Agent Architecture Patterns to grasp the fundamental concepts and design patterns.

2. Build with function calling (2-3 days)

   Work through Function-Calling Agents to create agents that can use tools and APIs.

3. Choose your framework (3-5 days)

   Study LLM Agent Frameworks to pick the right framework for your use case and understand the ecosystem.

4. Build your agent (ongoing)

   Apply these patterns to create agents for your specific use case, starting simple and adding complexity incrementally.

Common Agent Use Cases

AI agents excel at:

• Code Generation: Write, debug, and refactor code autonomously
• Data Analysis: Query databases, analyze data, generate insights
• Research: Search web, synthesize information, compile reports
• Customer Support: Answer queries, look up information, escalate issues
• Task Automation: Schedule meetings, send emails, update systems
• Testing & QA: Generate test cases, run tests, report issues
• Content Creation: Research topics, draft content, fact-check

Agent Capabilities

Throughout this learning path, you’ll implement agents that can:

• Use Multiple Tools: Call APIs, search databases, run code
• Plan Multi-Step Tasks: Break down complex goals into steps
• Handle Errors: Retry failed actions, adapt strategies
• Maintain Context: Remember conversation history and task state
• Make Decisions: Choose tools and actions based on situation
• Learn & Adapt: Improve based on feedback and results

Key Technologies

You’ll work with:

• LLM Providers: OpenAI (GPT-4, GPT-3.5), Anthropic (Claude), open models
• Agent Frameworks: LangChain, LlamaIndex, AutoGen, CrewAI
• Tools: APIs, databases, search engines, code execution
• Orchestration: ReAct, Plan-and-Execute, Chain-of-Thought
• Memory Systems: Conversation buffers, vector stores, graph databases

Prerequisites

Before starting this learning path, you should have:

• Python 3.9+ experience
• Understanding of LLMs and prompting
• Familiarity with APIs and async programming
• OpenAI API key (or alternative LLM provider)
• Basic understanding of software architecture

Production AI agents require careful consideration of: - **Cost control**: Agents can make many LLM calls - **Safety**: Limit tools and validate actions - **Monitoring**: Track agent behavior and performance - **Error handling**: Graceful failures and fallbacks - **User control**: Human-in-the-loop for critical actions

See LLM Security Best Practices and Cost Optimization for production guidance.

Agent Development Best Practices

1. Start Simple: Begin with single-tool agents before multi-tool systems
2. Test Thoroughly: Agents can behave unpredictably, test edge cases
3. Log Everything: Track agent reasoning, tool calls, and results
4. Set Limits: Maximum iterations, tool calls, and costs
5. Validate Tools: Ensure tool outputs are safe and expected
6. Plan for Failure: Agents will fail, design graceful degradation
7. Iterate: Build, test, observe, improve

Related Learning Paths

From Chatbot to Agent

Traditional Chatbot:

User: "Book me a flight to Paris"
Bot: "I can help you find flights. Visit airline.com to book."

AI Agent:

User: "Book me a flight to Paris"
Agent:
  1. Uses search tool → finds available flights
  2. Uses calendar tool → checks user's schedule
  3. Uses booking tool → reserves flight
  4. Uses email tool → sends confirmation
Result: "I've booked you on Flight AF123 departing May 15th at 10am.
         Confirmation sent to your email."

The difference? Agents act, not just respond.

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Ready to build? Start with Agent Architecture Patterns to understand the foundations.

Have questions? Open a discussion or check the contribution guide.