Researchers propose a system architecture that isolates instruction channels from data channels in LLM agents, preventing attackers from injecting malicious commands through user-supplied data inputs. This addresses a fundamental deployment constraint: current agents treat all text equally, allowing data to masquerade as instructions. Separating channels reduces the attack surface without requiring perfect prompt engineering or expensive fine-tuning. For production systems handling untrusted inputs—customer support agents, document processors, data extraction pipelines—this shifts security from reactive prompt hardening to structural defense. Operationally, this changes how agents are deployed. Rather than relying on instruction robustness, builders can now enforce data-instruction separation at the system level, reducing security review cycles and enabling safer delegation to less-controlled data sources. This likely reduces operational friction in compliance-sensitive verticals and makes multi-tenant agent deployments more feasible. Infrastructure changes needed: separate input handling paths and modified tokenization/routing logic, typically implementable in inference middleware rather than model retraining.