Are your AI applications still answering questions when they should be solving problems, making decisions, and adapting in real time? Traditional RAG pipelines often break under real-world pressure. They retrieve the wrong context, fail on multi-step tasks, and struggle when data changes, tools expand, and production demands rise.

Agentic RAG: Building Autonomous AI Systems shows how to move beyond basic retrieval-augmented generation and build autonomous AI systems that can plan, retrieve, reason, act, self-correct, and scale. This book brings together agentic workflows, advanced retrieval, GraphRAG, multi-agent orchestration, MCP integration, self-correcting loops, observability, and performance optimization into one practical guide for building reliable AI systems that work outside the lab. Its focus on autonomous agents, production-ready RAG architecture, vector databases, re-ranking, long-context optimization, and enterprise deployment makes it especially valuable for developers, AI engineers, data engineers, and technical founders.

Inside, readers will learn how to:

design agentic RAG systems that plan, observe, and adapt
improve retrieval quality with hybrid search, re-ranking, metadata filtering, and query transformation
build self-correcting AI workflows that reduce hallucinations and recover from failure
implement GraphRAG and multi-hop reasoning for connected knowledge
orchestrate multi-agent systems for research, synthesis, and decision support
optimize memory, latency, observability, and scaling for production deployment

If you want to build AI systems that do more than generate text, this book gives you the architecture, patterns, and engineering mindset to make it happen. Get your copy now and start building autonomous AI systems that are faster, smarter, and ready for real-world use.

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