Building Agentic AI Systems with DSPy: From Reasoning Modules to Tool-Using LLMs
Building Agentic AI Systems with DSPy: From Reasoning Modules to Tool-Using LLMs by Todd Chandler
English | June 5, 2025 | ISBN: N/A | ASIN: B0FC6X5WR1 | 261 pages | EPUB | 2.66 Mb
English | June 5, 2025 | ISBN: N/A | ASIN: B0FC6X5WR1 | 261 pages | EPUB | 2.66 Mb
Building Agentic AI Systems with DSPy: From Reasoning Modules to Tool-Using LLMs
Are you frustrated by fragile AI prototypes that can’t handle real-world complexity? Many developers struggle to weave large language models (LLMs) into reliable, tool-using agents. “Building Agentic AI Systems with DSPy” shows you how to transform those prototypes into robust, production-ready pipelines.
This book presents DSPy’s signature-based programming model, which bridges LLM reasoning and external tools—databases, vector stores, APIs—while enforcing type safety and runtime checks. You’ll learn to configure LLM backends, integrate MLflow for end-to-end tracing, and craft modules that validate inputs and guard against hallucinations. From constructing retrieval-augmented generation (RAG) workflows to implementing ReAct agents that alternate between “thought” and “action” steps, this guide equips you to build agents that actually solve problems.
You’ll gain hands-on skills and insights, including:
- Mastering DSPy’s @signature decorator to enforce input/output schemas and catch errors early
- Building custom tools—wrappers for REST APIs, database queries, and third-party SDKs—that slot seamlessly into agent loops
- Designing RAG pipelines: connecting vector stores, retrieving relevant context, and feeding it into prompt templates for accurate responses
- Implementing ReAct patterns: orchestrating LLM reasoning alongside actions, handling retries, and incorporating Assert and Suggest for self-correction
- Automating prompt and few-shot example tuning with DSPy’s Optimizer to maximize accuracy and minimize token costs
- Packaging agents into Docker containers, deploying to cloud platforms (AWS, GCP, Azure), and setting up CI/CD pipelines for continuous delivery
- Monitoring production systems: setting up MLflow tracking servers, capturing metrics, visualizing execution graphs, and debugging step by step
- Establishing self-improving loops that harvest user feedback, re-optimize pipelines in production, and ensure your agent evolves with changing data
- Securing workflows: fetching secrets from vaults, enforcing parameterized queries, and validating user inputs to prevent injections and data leaks