Over the past months, the way developers guide agentic AI coding tools has moved up several levels of abstraction, from phrasing prompts to engineering context to configuring the harness around the underlying model. In June 2026, practitioners began to describe a new level called loop engineering: Instead of prompting an agent interactively, developers design systems that prompt agents for them. The term spread rapidly, accompanied by bold claims and vocal skepticism, but its adoption in software projects has not yet been measured. We present (1) an exploratory review of the emerging gray literature on loop engineering, (2) a traceability analysis and an exploratory mining study of 36,710 software repositories, and (3) the design of a controlled experiment to study how loop goals, static analysis checks, and decision policies affect task success, code quality, resource usage, and token consumption of agent loops. Our literature review found broad agreement on what a well-engineered loop contains: agent loops started on a schedule or by repository events and bounded by machine-checkable stop conditions, persistent state files, verifier subagents, token budgets, and defined points of escalation to humans. These sources provide no systematic evidence of the claimed benefits. In the mining study, we found autonomous agent loops in 217 open-source GitHub repositories using heuristics and behavioral signals. Nearly all of these repositories committed the configuration that starts their loops, but almost none of them committed the state files that the gray literature suggests. Our proposed experiment is a first step toward closing the evidence gap identified in our literature review. We call for further empirical studies of the benefits and costs of loop engineering for software engineering tasks.

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