Two-Axis Method Helps Engineers Decide When to Keep AI Agent Jobs Local

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A software engineer working on a backend migration task faced repeated network drops and latency issues while running an AI agent loop that accessed local secrets stored in a dotenv file. The experience highlighted a broader problem: teams often misjudge where to run agent workloads because they never formally measure round-trip latency multiplied by the number of tool calls a job requires. A proposed two-axis placement framework evaluates jobs on secret surface exposure and round-trip tax to determine whether work should stay on the local machine or be offloaded to remote compute. The method includes a small Python scanner that classifies file paths as SECRET or PUBLIC and refuses to recommend remote placement if any sensitive files are in scope. The framework is presented as a decision aid with illustrative examples, not as a benchmark validated against production systems.

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