# Prompt-Induced Waste in Coding Agents: Reasoning, Effort, Harness Design, and End-to-End Cost

arXiv: https://arxiv.org/abs/2608.01347 · HTML: https://mlsanity.com/paper/2608.01347/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 2 Aug 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 6 of 6)

- [CostBench: Evaluating Multi-Turn Cost-Optimal Planning and Adaptation in Dynamic Environments for LLM Tool-Use Agents](https://mlsanity.com/paper/2511.02734/) (2025)
- [LLM Agents Already Know When to Call Tools -- Even Without Reasoning](https://mlsanity.com/paper/2605.09252/) (2026)
- [Toward Efficient Agents: Memory, Tool learning, and Planning](https://mlsanity.com/paper/2601.14192/) (2026)
- [CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning](https://mlsanity.com/paper/2411.16313/) (2024)
- [Not All LLM Reasoning is Visible in the Chain-of-Thought](https://mlsanity.com/paper/2607.22925/) (2026)
- [CORVUS: Context Optimization and Reduction Via Underlying Synchronization for LLM Coding Agents](https://mlsanity.com/paper/2607.22711/) (2026)

## Built on by (most cited first, 1 of 1)

- [What Does a Harness Repair? A Preregistered Study of Visibility, Baseline Adequacy and Evaluation Defects](https://mlsanity.com/paper/2610.05533/) (2026)

## Abstract (arXiv)

Coding-agent efficiency cannot be characterized by token count or model price alone. End-to-end cost and task success depend jointly on prompt semantics, inference effort, harness policy, model, task difficulty, tool use, context management, and provider accounting. Controlled experiments show that prompt wording can change reasoning and verification behavior without changing the task, that additional inference effort can help on difficult tasks but can also add cost without benefit, and that the value of an efficiency intervention can change when the harness changes. These results show that prompt, effort, and harness are interacting experimental factors rather than independent controls. We model efficiency as cost per successful task induced by the agent trajectory. Token and cache counts are measurements of that trajectory, not sufficient optimization targets. Agent evaluations should therefore measure success and end-to-end cost while controlling the system variables that determine how the trajectory is produced.