Do Ai Agents Know When A Task Is Simple Toward Com

---
name: do-ai-agents-know-when-a-task-is-simple-toward-com
description: "Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution - Large language model (LLM) agents increasingly automate multi-step engineering and informatics workflows, yet they rarely ask how much effort a task a..."
version: 1.0.0
author: Junjie Yin, Xinyu Feng
arxiv_id: 2607.13034
created: 2026-07-14
category: nlp-llm
tags: [cs.AI, cs.CL, cs.SE, eess.SY]
activation_keywords: [agents, know, task, simple, toward, complexity, aware, reasoning, execution, large]
---

# Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution

## Overview

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.

## Key Insights

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## Implementation Approach

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## Applications

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## Activation Keywords

agents, know, task, simple, toward, complexity, aware, reasoning, execution, large

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