Large language model agents have made strong progress on software engineering, yet current systems suffer from a context coupling problem: the standard code editing interface conflates code inspection, modification planning, and edit execution within a single context window, forcing agents to interleave exploratory viewing with strictly formatted edit generation. Irrelevant context accumulates and edit reliability degrades. We propose SWE-Edit, which decomposes the editing interface into two specialized subagents: a Viewer that extracts task-relevant code on demand, and an Editor that executes modifications from high-level natural language plans—letting the main agent focus on reasoning while delegating context-intensive operations to clean context windows. We further show that effective edit-format selection can be trained into a small model rather than requiring frontier-scale capacity: GRPO training on Qwen3-8B improves edit success by 12.5 pp and brings an 8B open-source editor to parity with GPT-5-nano on downstream SWE-Bench resolve rate, with the learned adaptive find-replace/whole-file-rewrite policy contributing primarily on the cost axis (-11.8% agent cost). To enable rapid editor iteration, we release PR-Edit, a lightweight editor-level evaluation that rank-orders the editor models we test consistently with their downstream SWE-Bench resolve rate. We release our code at https://github.com/microsoft/SWE-Edit.