# From Question Answering to Task Completion: A Survey on Agent System and Harness Design

- **arXiv**: 2606.20683v1
- **Published**: 2026-06-14
- **Categories**: cs.AI, cs.CL
- **Authors**: Jianyuan Guo, Zhiwei Hao, Chengcheng Wang, Cheng Fan, Tingzhang Luo, Hongguang Li, Ying Gao, Hefei Mei, Jiankun Peng, Rongjian Xu, Minjing Dong, Han Wu, Mengyu Zheng, Kai Han, Shiqi Wang, Chang Xu, Yunhe Wang
- **Affiliations**: City University of Hong Kong, University of Sydney, Peking University, TokenRhythm Technologies
- **Repository**: https://github.com/ggjy/Awesome-Agent-Engineering

## Abstract

LLM-based agents mark a shift from passive question answering to active task completion: they perceive environments, invoke tools, maintain state, and act over extended horizons. As agent systems have evolved from prompt engineering to workflows and context engineering, harness engineering, and agent-native training with co-evolution, a central question has become increasingly important: where does the bottleneck in agent performance reside—in the foundation model, in the execution harness, or in the coupling between them? This survey examines LLM-based agents through a model–harness lens. We first clarify the functional definition of agents and the implementation view of an LLM-based agent as a foundation model coupled with an execution harness. We then analyze the limits of model-centric scaling, trace four paradigms of agent engineering, and decompose the execution harness into six coupled runtime responsibilities: observation, context, control, action, state, and verification/governance. Using this decomposition, we map task properties and domain pressures to harness configurations, review benchmark and evaluation practices, and synthesize model–harness evidence on how runtime design affects long-horizon task completion, efficiency, and reliability. Finally, we identify open challenges in value-aware evaluation, safety, harness generalization, and model–harness co-evolution.

## Key Contributions

1. **Model-Harness Lens**: formal definition Agent = ⟨M, H⟩, where M is model layer and H is execution harness with six components
2. **Four Paradigms of Agent Engineering**: prompt engineering → context engineering → harness engineering → agent-native training & co-evolution
3. **Harness Anatomy**: six coupled runtime components — I_obs (observation), C (context), L (control loop), I_act (action), S (state/artifact store), V (verification/governance)
4. **Harness-Aware Task Taxonomy**: task complexity L1-L4 mapped to harness bottlenecks
5. **Cross-Layer Interactions**: harness as coupled systems problem, not independent module optimization
