Persistent context files (AGENTS.md, CLAUDE.md) are standard practice for guiding AI coding agents, yet evidence for their effectiveness is contradictory. We present a controlled ablation of context-injection strategy across two frontier agents (Claude Code and Codex), 17 real tasks from 3 repositories, and 288 evaluated runs with gold-test evaluation. Context strategy does not measurably move correctness on either agent (bounded to ≤10–15pp via equivalence testing). A failure-mode triage reveals why: agents fail on implementation skill—feature design, pattern selection, exact wiring—not missing repository knowledge that a context file could supply; a manipulation probe confirms the real AGENTS.md never converts a near-miss to a pass on either agent. We further show that borderline task difficulty is agent-specific (Spearman 𝜌=0.75), offering a candidate explanation for prior contradictions: single-agent studies draw tasks from different agents’ informative bands. We release all code, data, and analysis.