Four-Signal Framework Proposed to Route AI Agent Tasks Between Local and Remote Execution

A backend engineer has outlined a practical decision framework for teams running local-first AI agents, addressing the challenge of deciding which tasks should execute on-device versus in the cloud. The proposed system evaluates four signals — network latency, secret classification, offline capability, and burst need — to determine the appropriate execution lane for each job. Tasks containing sensitive data such as tokens, private keys, or customer records must remain on local disk, while stripped or redacted payloads may be routed remotely. The framework emphasizes that placement logic should live in the control plane and produce deterministic, auditable decisions rather than relying on ad hoc prompts or vendor settings. A Python-based gate, decision table, and accompanying tests are provided so teams can implement the split independently of any specific product or cloud provider.