From Question Answering to Task Completion: A Survey on Agent System and Harness Design
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 ag...
| Authors | Jianyuan Guo, Zhiwei Hao, Chengcheng Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 14 June 2026 |
| Last updated at source | 14 June 2026 |
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