LlamaIndex Data Agents: Create Production-Ready AI Assistants for Documents, Databases, APIs, and Enterprise Workflows
Build AI assistants that work with real business data, not fragile chatbot demos that fall apart outside a test prompt.
Are your documents, databases, APIs, reports, and internal tools scattered across too many systems? Do you want to build AI agents that can retrieve evidence, answer business questions, use tools, query SQL data, and support enterprise workflows with more control?
LlamaIndex Data Agents gives developers and technical teams a practical path for building production-ready AI assistants using LlamaIndex. Instead of stopping at basic RAG, this book shows how to design data agents that can search documents, prepare business data, use query engines, call tools, work with SQL databases, combine multiple data sources, and support safer workflow automation.
Inside, you'll learn how to:
- Set up a clean Python and LlamaIndex project
- Load PDFs, text files, Markdown, CSV, JSON, and web data
- Build reliable retrieval pipelines and query engines
- Create document Q&A assistants with source-aware answers
- Design tool-using agents for search, calculation, extraction, and validation
- Build SQL and structured data agents for business questions
- Combine documents, databases, and APIs in hybrid workflows
- Use LlamaParse and LlamaCloud for document intelligence
- Add multi-agent workflows, human approval, validation, logging, evaluation, FastAPI deployment, Docker, and monitoring
This book is written for Python developers, AI engineers, software engineers, data engineers, analytics engineers, and technical builders who want practical, maintainable AI systems over real data.
