1.8 KiB
1.8 KiB
source_url, ingested, sha256
| source_url | ingested | sha256 |
|---|---|---|
| user-upload | 2026-05-23 | unknown |
Agent Harness Engineering: A Survey
Metadata
- Authors: Junjie Li^1,6^, Xi Xiao^6^, Yunbei Zhang^5^, Chen Liu^2^, Lin Zhao^4, Xiaoying Liao^3, Yingrui Ji^6, Janet Wang^6, Jianyang Gu^7, Yingqiang Ge^9, Weijie Xu^9, Xi Fang^9, Xiang Xu^9, Tianchen Zhao^9, Youngeun Kim^9, Tianyang Wang^6, Jihun Hamm^5, Smita Krishnaswamy^2, Jun Huan^9, Chandan K Reddy^8,9
- Institutions: 1 CMU, 2 Yale, 3 JHU, 4 NEU, 5 Tulane, 6 UAB, 7 OSU, 8 Virginia Tech, 9 Amazon
- Venue: Under review at TMLR (Transactions on Machine Learning Research), 2026
- Project Page: Awesome-Agent-Harness
Abstract
The rapid deployment of large language model (LLM) agents in production has revealed a recurring pattern: task execution reliability depends less on the underlying model than on the infrastructure layer that wraps it — the agent execution harness. This survey provides a practice-grounded, systematic treatment of agent harness engineering, organized around three claims:
- Binding-Constraint Thesis: The agent harness is an independent system layer whose engineering quality drives a large share of real-world reliability
- ETCLOVG Taxonomy: A seven-layer taxonomy (Execution environment, Tool interface, Context management, Lifecycle/Orchestration, Observability, Verification, Governance)
- Ecosystem Mapping: 170+ open-source projects mapped onto this taxonomy
Key Contributions
- Three-phase engineering evolution: Prompt → Context → Harness Engineering
- Cross-layer synthesis: Cost-Quality-Speed Trilemma, Capability-Control Tradeoff, Harness Coupling Problem
- Open-problem agenda spanning harden/scale execution, maintain reliable state, diagnose from traces, standardize handoffs, and adaptive simplification