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

Source: [arXiv](https://arxiv.org/abs/2607.13034v1)  
Feed7 permalink: https://feed7.dev/p/2607-13034v1-03g7ghx  
Published: 2026-07-14T17:59:31.000Z  
Trust: Needs Review (needs_review)

## Why Included

E3 makes agents estimate task scope, try the minimum viable path, and expand only after verification fails. In a controlled edit benchmark, it preserved task completion while sharply reducing work.

## Source Summary

E3 follows Estimate, Execute, Expand: choose an initial scope, attempt the shortest reliable path, then widen it only if verification fails. On **121 deterministic edits**, it matched the strongest baseline’s **100% task success** while cutting cost 85%, tokens 91%, and inspected files 92%.

## Practical Implication

Add scope estimation before retrieval-heavy coding loops, and make failed verification the trigger for reading more files or dependencies. Track redundant inspection alongside correctness so one-line changes do not automatically become repository audits.

## Agent-Ready Context

E3 follows Estimate, Execute, Expand: choose an initial scope, attempt the shortest reliable path, then widen it only if verification fails. On **121 deterministic edits**, it matched the strongest baseline’s **100% task success** while cutting cost 85%, tokens 91%, and inspected files 92%.

Add scope estimation before retrieval-heavy coding loops, and make failed verification the trigger for reading more files or dependencies. Track redundant inspection alongside correctness so one-line changes do not automatically become repository audits.

MSE-Bench is a capability-controlled simulator, not a deployed-agent measurement. A gpt-4o case study on a real library showed the same direction at comparable task success, but its evidence is narrower and one run was limited by a provider rate limit.

## Context Map

- Layer: agent
- Domains: coding
- Topics: harness-engineering, agent-reliability, context-engineering

## Uncertainty

- MSE-Bench is a capability-controlled simulator, not a deployed-agent measurement. A gpt-4o case study on a real library showed the same direction at comparable task success, but its evidence is narrower and one run was limited by a provider rate limit.

## Agent Instruction

Use this item as source-backed context. Do not invent claims beyond the linked source. If this item conflicts with another source, call out the conflict.