Probabilistic Machine Learning in Practice: Construct Robust AI Agents with Deterministic Guardrails for Real-World Deployment

Are your AI agents impressive in demos but unreliable in production? If model outputs drift, tool calls break, or edge cases trigger unsafe behavior, you do not have an AI deployment problem—you have an architecture problem.

Probabilistic Machine Learning in Practice shows you how to build AI systems that reason under uncertainty while still operating safely inside deterministic software environments. This book presents a practical engineering approach for combining probabilistic machine learning with strict guardrails such as schema validation, state machines, retry boundaries, circuit breakers, redaction pipelines, and human-in-the-loop controls—so your agents can work in real workflows without creating operational risk.

You’ll learn how to build robust AI agents that can:

• measure uncertainty and route decisions safely

• enforce structured outputs and secure tool-calling contracts

• manage state, retries, and failure handling in multi-step workflows

• integrate retrieval, memory, and multi-agent orchestration patterns

• test, evaluate, and deploy agent systems for enterprise use cases

Written for software engineers, AI engineers, systems architects, and technical operators, this book focuses on working implementations and production-ready patterns rather than fragile prototypes. Want systems that can reason probabilistically but still behave predictably when it matters most? This is the blueprint.