Overview

Once you understand the foundations of DSPy, the next step is learning how to build scalable, self-improving, production-grade AI systems. This advanced volume shows you exactly how to architect complex agentic workflows, implement optimization loops, orchestrate multi-agent systems, and deploy DSPy agents in real MLOps environments. Advanced DSPy for Agentic Workflows takes you deep into DSPy's most powerful capabilities-optimization, multi-agent orchestration, tool architectures, advanced RAG, multimodal processing, evaluation frameworks, and enterprise deployment patterns. This is the book that shows you how to build real AI systems, not toy examples. What You'll Learn How to architect multi-step, multi-agent DSPy pipelines How to build dynamic, tool-using agents that call APIs, tools, and services How to design advanced RAG systems: multi-hop, hierarchical, hybrid retrieval How to use BootstrapFewShot, MIPRO, and tuners to create self-improving agents How to implement guardrails, auto-correction, and self-healing systems How to integrate multimodal workflows (vision, audio, documents) How to build real-time, event-driven, and streaming DSPy agents How to track performance with MLflow, Redis, dashboards, and metrics How to deploy scalable, fault-tolerant agent infrastructure in production How to optimize for cost, speed, reliability, and enterprise scale Who This Book Is For AI engineers and ML developers DSPy users ready to master optimization and multi-agent systems Teams deploying AI systems in production environments Architects designing scalable enterprise workflows This volume gives you production-level expertise-the patterns, architectures, tuners, safety systems, and MLOps tooling required to operate DSPy agents in the real world. If you want to design advanced agent pipelines, build self-improving systems, and deploy scalable AI architectures, this is the definitive guide.

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