Large language model (LLM) agents are increasingly automating multi-step engineering and informatics workflows, yet they rarely assess the actual effort required for a task. They often employ a maximum-context-first strategy—re-reading files and dependencies already seen—transforming a simple one-line edit into a small code-base audit. We argue that the missing capability is task-aware execution-scope estimation: determining a task's difficulty, the information it truly needs, and the shortest reliable path before committing budget.
We formalize minimum-sufficient execution and the Agent Cognitive Redundancy Ratio (ACRR), proposing E3 (Estimate, Execute, Expand): the agent estimates an initial operating point, executes a minimum viable path, and expands scope only when verification fails. On MSE-Bench—a deterministic benchmark of 121 edits in a capability-controlled simulator—E3 matches the strongest baseline's 100% success while cutting costs by 85%, tokens by 91%, and inspected files by 92%. It further outperforms a strong adaptive retrieval baseline by 16%; these gains persist across held-out instruction wording and nearly every cost weighting. A companion real-model harness (LLM-Case) corroborates the effect on a live gpt-4o agent editing a real open-source library, with every candidate patch graded by running the project's real pytest suite against a measured oracle: the over-reading is milder but real, and E3 is the leanest and fastest policy at comparable task success—the only shortfall being a provider rate-limit, not a wrong edit.
We frame this as a controlled probe of execution redundancy rather than a measurement of any deployed agent, positioning task-aware execution as a step toward engineering-grounded AI (EGAI)—agents whose effort is anchored in the engineering reality of the task. We release the framework and benchmark.
Blogger's Review: The E3 framework proposed in this paper effectively reduces redundancy in AI agent task execution, marking significant progress in complexity-aware reasoning. This offers new insights into efficient AI applications, particularly in engineering, making it worthy of attention for real-world impacts.