Building Production-Ready AI Agents with LlamaIndex

From First Query to Multi-Agent Systems: A Practical Guide for Python Developers

Introduction: Why Agents, Why LlamaIndex, Why Now

1. The Agent Moment
2. What Production-Ready Really Means
3. Why LlamaIndex Over Alternatives
4. How to Read This Book

Chapter 1: LLM Fundamentals for Agent Builders

1. How Large Language Models Generate Text
2. Tokens, Context Windows and the Cost Equation
3. Prompting Patterns That Matter for Agents
4. Model Selection: Open Source vs Proprietary Trade-offs
5. Setting Up Your First LLM Connection with LlamaIndex

Chapter 2: Inside LlamaIndex – Architecture and Core Concepts

1. The LlamaIndex Abstraction Stack
2. Documents, Nodes and the Ingestion Pipeline
3. Settings and Global Configuration
4. The Callback System and Extensibility Points
5. Project Setup: Installation, Virtual Environments and Dependencies

Chapter 3: Data Ingestion and Indexing

1. Loading Data from Multiple Sources
2. Node Parsers and Chunking Strategies
3. Metadata Extraction and Enrichment
4. The Ingestion Pipeline API
5. Building Your First Index

Chapter 4: Embeddings and Vector Stores

1. How Embeddings Represent Meaning
2. Choosing an Embedding Model
3. Vector Store Landscape and Selection Criteria
4. Implementing Your First Vector Store
5. Index Persistence and Versioning

Chapter 5: Retrieval Strategies for Production RAG

1. Top-K Retrieval and Its Limitations
2. Hybrid Search: Combining Semantic and Keyword
3. Reranking and Relevance Filtering
4. Metadata Filtering and Auto-Retrieval
5. Node Post-Processors for Fine-Tuning Results

Chapter 6: Building Your First RAG Application

1. Project Architecture and Design Decisions
2. Data Pipeline Implementation
3. Retrieval and Query Engine Setup
4. Adding Conversational Memory
5. Testing the Complete System

Chapter 7: Tools, Function Calling and Structured Outputs

1. The Tool Abstraction in LlamaIndex
2. Defining Custom Tools with Type Signatures
3. Function Calling and Model Support
4. Structured Outputs with Pydantic Programs
5. Error Handling and Retry Logic for Tool Calls

Chapter 8: Agent Workflows and Orchestration

1. Event-Driven Architecture Fundamentals
2. Defining Steps and Events
3. State Management with Context Objects
4. Parallel Execution and Conditional Routing
5. Streaming Progress Events to Users

Chapter 9: Memory, Planning and Reasoning

1. Short-Term vs Long-Term Memory Architectures
2. ChatMemoryBuffer and VectorMemoryBlock
3. Fact Extraction and Knowledge Accumulation
4. ReAct: Reasoning and Acting in Loops
5. Self-Reflection and Iterative Improvement

Chapter 10: Multi-Agent Systems

1. Why Multi-Agent Architectures
2. AgentWorkflow: Linear Swarm Pattern
3. Orchestrator Agent: Sub-Agents as Tools
4. Custom Planner: Maximum Flexibility
5. Building a Multi-Agent Research Assistant (Project #2)

Chapter 11: Evaluation, Testing and Debugging

1. Defining What Good Looks Like
2. Retrieval Evaluation Metrics
3. Generation Quality Assessment
4. Automated Testing Strategies
5. Debugging Common Failure Modes

Chapter 12: Observability and Monitoring

1. The Three Pillars of Observability
2. OpenTelemetry Integration with LlamaIndex
3. Tracing Agent Execution Paths
4. Metrics, Dashboards and Alerting
5. Cost Tracking and Token Accounting

Chapter 13: Performance Optimization and Scalability

1. Caching Strategies for Embeddings and LLM Calls
2. Async Programming Patterns in Production
3. Query Optimization Techniques
4. Scaling Infrastructure: From Single Server to Distributed
5. Cost Optimization Without Sacrificing Quality

Chapter 14: Security, Deployment and Production Patterns

1. Security Considerations for AI Agent Systems
2. Building Production APIs with FastAPI
3. Containerization with Docker
4. Cloud Deployment Strategies
5. Real-World Production Patterns and Anti-Patterns

Conclusion: The Road Ahead

1. What We Built
2. The Evolving Agent Landscape
3. Principles That Will Endure
4. Your Next Steps

References