Harness Engineering: Designing the Systems That Make AI Coding Agents Reliable

Harness Engineering: Building and Controlling Autonomous AI Coding AgentsModel capability does not guarantee reliable software outcomes. This book is for hands-on software engineers and technical leaders who want to move from ad hoc AI coding assistance to harness engineering—designing the execution system around the model: tools, control loops, state and continuity, verification, observability, recovery, and governance.Across foundations, design and build, operate and scale, and governance and adoption you will learn how to:Define the split between model reasoning and harness design, and build minimum viable control loops with checks and escalations.Set acceptance criteria, failure thresholds, and escalation rules before you automate; then implement a baseline single-agent harness with approvals, tests, and policy checks.Introduce multi-agent roles and handoffs with explicit contracts, and structure repository-native artefacts for consistent retrieval and execution.Combine deterministic checks with independent evaluation in verification pipelines; instrument cost and throughput; and design interruption-safe, resumable workflows.Apply least privilege, approvals, and safety boundaries for risky actions and secrets.Encode standards as deterministic checks, choose metrics that reflect reliability and maintainability as well as throughput, and roll harness practices out across teams with clear ownership—including a ninety-day adoption roadmap.The tone is practical and source-aware: verification pairs deterministic checks with independent evaluation; governance is treated as an architectural default, not a late add-on; and autonomy is discussed with explicit human checkpoints and measurement—without promising unsupervised production coding.If you need a disciplined, engineering-first approach to agentic coding workflows, this book delivers the concepts, patterns, and operating discipline to match. Read more