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Harness Engineering
“Most people do not know that they can just point their agents at my writing,<br>tweets, podcasts, and talks and improve the output of their agents by 100x.”
— Ryan Lopopolo
Harness engineering, the practice of improving agent output by shaping the<br>environment around it, holds a chosen model and coding agent constant as a black<br>box. It improves the two external levers—context and tools—and curates the<br>environment around them. The worker should be able to recover intent, operate<br>the real system, respect authority, prove the outcome, and leave the next run<br>better equipped.
A central purpose of that environment is to carry an organization's<br>nonfunctional requirements: the quality attributes and constraints governing<br>reliability, security, compatibility, maintainability, performance, operability,<br>risk posture, and polish. The harness also carries local decisions about how to<br>prioritize, trade off, and satisfy those requirements. Ryan adopted a<br>systems-level framing from 2026’s [un]prompted conference that describes this<br>as getting the whole universe of nonfunctional requirements into code. Make the<br>Repository Teach the Agent develops how the requirements and decisions become<br>retrievable context, examples, tools, and executable constraints.
Because work is an iterative game, a harness can make organizational judgment<br>cumulative. Lessons from accepted work, corrections, failures, and user<br>responses become context, boundaries, tools, examples, and checks that shape<br>later trajectories. Over time, that feedback loop can make coherence<br>cumulative across agent-maintained artifacts.
Code is how an agent uses a computer. That internal action language can<br>produce reliable domain outcomes for people who never review the implementation<br>when last-mile deployment supplies the organization’s context, capabilities,<br>authority, and proof.
General model weights contain only the visible tip of an organization’s<br>process-data iceberg. Below the waterline sit the current operational state,<br>local ontology, quality bar, procedures, exception history, and authority<br>relationships that an agent needs to do a particular job. Organizations cannot<br>presume that this private, changing process data will be present in general<br>model weights, nor that agents will reliably intuit which process data matters<br>to the organization. Harness engineering is the last-mile work of making it<br>available to a capable worker as context and tools.
Point a coding agent at this repository alongside the system it should improve.<br>AGENTS.md routes the task to the relevant arguments, cases, and proof. For<br>direct reading, start with the thesis index. For an application, choose from<br>the playbooks.
Sources and related work
“Harness engineering: leveraging Codex in an agent-first world” (fetch<br>helper for agents blocked by the canonical page)
Source library
Influences and alternate framings
Repository-authored material is licensed under CC BY 4.0. See COPYING.md<br>for attribution and rights in source material.
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Ryan Lopopolo’s anthology, field guide, and agent context bundle for harness engineering
openai.com/index/harness-engineering/
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