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Start Your AI Agent Development Journey

Welcome to the AI Agents Plugin! This interactive guide helps you master AI agent development with production-grade patterns from LangChain, Anthropic, and OpenAI.

Choose Your Learning Path

1. Agent Fundamentals

Master the core concepts of AI agent systems.

Topics covered:

• ReAct (Reasoning + Acting) pattern
• Plan-and-Execute architecture
• Cognitive loops and decision making
• Single vs multi-agent systems

Skills you'll learn:

• Design agent architectures
• Implement ReAct agents
• Add circuit breakers and error handling
• Build autonomous task solvers

Get started:

"Help me understand how ReAct agents work"
"Build a simple agent with LangGraph"
"What's the difference between ReAct and Plan-and-Execute?"

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2. LLM Integration

Connect and optimize Large Language Models for your agents.

Topics covered:

• Anthropic Claude API (Messages, Tool Use)
• OpenAI API (Chat Completions, Functions)
• Prompt engineering and templates
• Cost optimization strategies

Skills you'll learn:

• Integrate multiple LLM providers
• Design effective system prompts
• Implement streaming responses
• Reduce API costs by 40-60%

Get started:

"Show me how to use Claude with tool calling"
"Design a chain-of-thought prompt template"
"How do I implement retry with exponential backoff?"

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3. RAG Systems

Build Retrieval-Augmented Generation for grounded responses.

Topics covered:

• Embedding models and vector stores
• Chunking strategies (semantic, recursive)
• Hybrid search (dense + sparse)
• Reranking and evaluation

Skills you'll learn:

• Design RAG pipelines
• Choose optimal chunking strategies
• Implement hybrid search
• Evaluate with RAGAS metrics

Get started:

"Build a RAG system with Pinecone"
"What's the best chunk size for technical docs?"
"How do I implement reranking?"

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4. Tool Calling

Enable LLMs to interact with external systems.

Topics covered:

• Claude Tool Use
• OpenAI Function Calling (with strict mode)
• JSON Schema design
• Error handling and validation

Skills you'll learn:

• Design tool schemas
• Implement tool loops
• Handle execution errors
• Orchestrate sequential/parallel tools

Get started:

"Create a tool for database search"
"How does OpenAI strict mode work?"
"Best practices for tool descriptions"

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5. Multi-Agent Systems

Build coordinated systems of specialized agents.

Topics covered:

• Orchestrator-Worker pattern
• Hierarchical architectures
• Agent communication
• LangGraph multi-agent

Skills you'll learn:

• Design multi-agent architectures
• Implement orchestration patterns
• Coordinate parallel execution
• Handle multi-agent failures

Get started:

"Build an orchestrator-worker system"
"How does agent communication work?"
"Design a hierarchical agent system"

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6. Agent Memory

Give agents the ability to remember and learn.

Topics covered:

• Short-term (conversation buffer)
• Long-term (vector store)
• Semantic and episodic memory
• Context window management

Skills you'll learn:

• Implement multi-layer memory
• Design retrieval strategies
• Optimize context usage
• Build personalized agents

Get started:

"Add conversation memory to my agent"
"How do I implement semantic memory?"
"Best practices for context management"

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7. Agent Safety

Ensure responsible AI agent deployment.

Topics covered:

• Input/output guardrails
• Prompt injection defense
• Content filtering
• Rate limiting and compliance

Skills you'll learn:

• Implement safety guardrails
• Detect and prevent attacks
• Add monitoring and logging
• Meet compliance requirements

Get started:

"Add guardrails to my agent"
"How do I prevent prompt injection?"
"Implement rate limiting for agents"

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Quick Commands

Command	Description
/learn	This guide
/roadmap [topic]	View topic details
/assess [topic]	Test your knowledge
/resources [topic]	Get learning resources
/project [topic] [level]	Find project ideas

Learning Path by Experience

Beginner (0-2 months)

1. Start with Agent Fundamentals
2. Learn LLM Integration basics
3. Build first ReAct agent
4. Practice with Tool Calling

Intermediate (2-6 months)

1. Master RAG Systems
2. Implement Multi-Agent patterns
3. Add Agent Memory
4. Build production agents

Advanced (6+ months)

1. Complex orchestration patterns
2. Agent Safety and compliance
3. Performance optimization
4. Production deployment

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Framework Quick Reference

Framework	Best For	Key Feature
LangChain	General agents	Tool ecosystem
LangGraph	Complex workflows	State machines
Anthropic SDK	Claude agents	Native tool use
OpenAI SDK	GPT agents	Strict mode

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Ready to start? Tell me which path interests you! 🚀