VentureBeat published analysis on 13 September documenting a structural vulnerability in enterprise agentic deployments: compliance rules embedded in system prompts decay through the "lost in the middle" phenomenon as agents accumulate context over multi-day workflows. Instructions placed at the beginning of a system prompt become statistically de-prioritised relative to more recent context as token volume grows, with no error signal, no alert, and no audit trail. The agent continues generating outputs normally while violating the governance constraints it was initialised with. The failure mode is silent by design — the agent is not malfunctioning, it is operating on its current context distribution, in which the original compliance instructions are underweighted. The proposed architectural remediation is neuro-symbolic separation: moving all hard compliance logic outside the LLM context window entirely, into a deterministic rule engine that validates every agent output before execution. Specific implementation steps: latent checkpointing audits to detect when system-prompt instructions have drifted below effective influence threshold; physical separation of agent working memory from governance constraints; and scheduled context resets that reintroduce governance rules at their original position weight. The longer-context-window assumption — that expanded context windows solve context-loss problems in governance applications — is categorically wrong for this use case. Larger windows increase the distance over which the "lost in the middle" effect operates; they do not reduce it.