How do you efficiently steer AI coding agents?

Effective coding-agent steering requires more than adding extensive repository instructions, as research on files such as AGENTS.md shows mixed effects on task success, costs, runtime, and model behavior despite potential efficiency gains. The central argument is that instructions cannot prove compliance, so engineering expectations should be distributed across five distinct responsibility layers: natural-language agent guidance for context and tradeoffs, executable checks for verifiable conditions, independent review for judgment, lifecycle systems for keeping evidence current, and accountable humans for approvals and exceptions. Drawing on the Software Standards Bootstrap project, the author describes generating repository-specific, provenance-rich guidance from pinned code snapshots while clearly separating active standards, proposed standards, verification commands, skills, and automation proposals. Expectations should be classified according to whether they require interpretation, can be mechanically tested, depend on a particular revision, may become invalid after changes, or require human authority. The recommended approach is to make these boundaries explicit, attach evidence and checks to the relevant repository state, and use guidance as a routing mechanism rather than treating it as proof that software standards have been met.

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