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

Noosaga Placements

• Artificial Intelligencesubfield90%
  The paper addresses a core AI problem: enabling agents to estimate task difficulty and plan efficient execution, which falls under artificial intelligence broadly.
  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.position task-aware execution as a step toward engineering-grounded AI (EGAI)
• Large Language Modelsframework90%
  The paper directly uses large language models (LLMs) as the core component of the agent, and the E3 framework is built on top of LLM-based agents.
  Large language model (LLM) agents increasingly automate multi-step engineering and informatics workflowsA companion real-model harness (LLM-Case) corroborates the effect on a live gpt-4o agent
• Natural Language Processingsubfield80%
  The work focuses on LLM agents, which are a core NLP application, and uses language models for code editing tasks.
  Large language model (LLM) agents increasingly automate multi-step engineering and informatics workflowsA companion real-model harness (LLM-Case) corroborates the effect on a live gpt-4o agent editing a real open-source library
• Foundation Modelsframework60%
  The paper positions its work within the broader context of foundation models, as LLMs are a type of foundation model, and the E3 framework aims to improve their efficiency.
  position task-aware execution as a step toward engineering-grounded AI (EGAI)
• Software Engineeringsubfield70%
  The paper targets software engineering tasks (code editing) and evaluates on a benchmark of edits in a simulator and real open-source library.
  On MSE-Bench--a deterministic benchmark of 121 edits in a capability-controlled simulatorA companion real-model harness (LLM-Case) corroborates the effect on a live gpt-4o agent editing a real open-source library

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.

Paper Context