The Last Harness You'll Ever Build

Computer Science > Artificial Intelligence [Submitted on 22 Apr 2026] The Last Harness You'll Ever Build Haebin Seong, Li Yin, Haoran Zhang AI agents are increasingly deployed on complex, domain-specific workflows -- navigating enterprise web applications that require dozens of clicks and form fills, orchestrating multi-step research pipelines that span search, extraction, and synthesis, automating code review across unfamiliar repositories, and handling customer escalations that demand nuanced domain knowledge. \textbf{Each new task domain requires painstaking, expert-driven harness engineering}: designing the prompts, tools, orchestration logic, and evaluation criteria that make a foundation model effective. We present a two-level framework that automates this process. At the first level, the \textbf{Harness Evolution Loop} optimizes a worker agent's harness \mathcal{H} for a single task: a Worker Agent W_{\mathcal{H}} executes the task, an Evaluator Agent V adversarially diagnoses failures and scores performance, and an Evolution Agent E modifies the harness based on the full history of prior attempts. At the second level, the \textbf{Meta-Evolution Loop} optimizes the evolution protocol \Lambda = (W_{\mathcal{H}}, \mathcal{H}^{(0)}, V, E) itself across diverse tasks, \textbf{learning a protocol \Lambda^{(\text{best})} that enables rapid harness convergence on any new task -- so that adapting an agent to a novel domain requires no human harness engineering at all.} We formalize the correspondence to meta-learning and present both algorithms. The framework \textbf{shifts manual harness engineering into automated harness engineering}, and takes one step further -- \textbf{automating the design of the automation itself}. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.21003 [cs.AI]   (or arXiv:2604.21003v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.21003 Focus to learn more Submission history From: Haebin Seong [view email] [v1] Wed, 22 Apr 2026 18:51:48 UTC (121 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)