Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution
2026-07-14 • Artificial Intelligence
Artificial IntelligenceComputation and LanguageSoftware Engineering
AI summaryⓘ
The authors study how large language model (LLM) agents handle tasks that need multiple steps, like coding edits, and notice these agents often do extra unnecessary work by rereading too much information. They propose a new approach called E3, where the agent first estimates how much it really needs to do, performs the minimal necessary actions, and only expands its scope if needed. On a test benchmark and a real coding task, their method achieves the same success rates as the best methods but uses far fewer resources and time. Their work highlights the importance of agents understanding the actual effort a task requires to be more efficient.
Large Language ModelsExecution ScopeTask Difficulty EstimationMulti-step WorkflowsCode EditingAgent Cognitive Redundancy RatioAdaptive ExecutionE3 MethodBenchmarkingEngineering-Grounded AI
Authors
Junjie Yin, Xinyu Feng
Abstract
Large language model (LLM) agents increasingly automate multi-step engineering and informatics workflows, yet they rarely ask how much effort a task actually requires. They often follow a maximum-context-first strategy--re-reading files and dependencies they have already seen--turning a one-line edit into a small code-base audit. We argue the missing capability is task-aware execution-scope estimation: judging 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), and propose 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 cost by 85%, tokens by 91%, and inspected files by 92%, and further beats a strong adaptive retrieval baseline by 16%; the gains survive held-out instruction wording and essentially 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 actually 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--its one shortfall a provider rate-limit, not a wrong edit. We frame this as a controlled probe of execution redundancy, not a measurement of any deployed agent, and position 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.