arXiv:2604.21003v1 Announce Type: new
Abstract: 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}.