Harness Engineering

Design Patterns for Securing Long-Horizon Multi-Agent AI Systems

By: Ken Huang

Narrated by: Virtual Voice

Harness Engineering is a practical guide to designing secure control planes for long-horizon, multi-agent AI systems. It explains how to move beyond prompt-only safety and build the durable harness around agents: autonomy controls, identity, intent contracts, context boundaries, memory governance, observability, runtime steering, scalability, data governance, and evaluation-driven improvement.

Written for engineers, security leaders, architects, researchers, and advanced students, the book treats agent safety as an engineering discipline. Each chapter presents design patterns, failure modes, reference architectures, and implementation lessons for teams building agents that use tools, remember context, coordinate with other agents, and operate over extended tasks.

The central thesis is simple: capable models are not enough. Production agent systems need a secure harness that scopes what agents may do, verifies why they are acting, limits access to tools and data, records decisions, and improves through measurable feedback. The book connects secure-by-design principles with modern agentic AI practice, showing how hill climbing, evaluation, observability, and recursive improvement can be governed rather than left accidental.

Harness Engineering is for readers who want a coherent pattern language for building trustworthy AI agent systems in the real world.