Harness Engineering
Designing the Systems That Make AI Coding Agents Reliable
Harness Engineering: Building and Controlling Autonomous AI Coding Agents
Model capability does not guarantee reliable software. This book is for hands-on software engineers and technical leaders who want to move from ad hoc AI coding assistance toharness engineering—designing the execution system around the model: tools, control loops, state and continuity, verification, observability, recovery, and governance.
From foundations through governance and adoption—including conclusion and glossary chapters—you will learn how to:
- Define responsibilities 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 strict contracts, and structure repository-native artefacts for consistent retrieval and execution.
- Combine deterministic and inferential sensors 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 speed, 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—no promise of 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.
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