SKILL.nb: Selective Formalization and Gated Execution for Durable Agent Workflows

Title:SKILL.nb: Selective Formalization and Gated Execution for Durable Agent Workflows

Abstract:AI agents increasingly turn past experience into reusable artifacts such as code, workflows, and procedural memories. Reuse can improve efficiency, but it also creates a lifecycle reliability problem: artifacts that succeed once may fail under environment drift, underspecified tasks, or changing task distributions, especially in web automation. We introduce this http URL, a framework for governing reusable agent workflows with evidence-calibrated lifecycle policies. this http URL uses selective formalization: execution evidence decides which workflow steps should become executable code, which should remain natural-language guided, and when those choices should be revised. Workflows are stored as auditable, versioned notebooks that interleave natural-language guidance, multi-language executable cells, validation gates, fallback paths, and multimodal evidence such as outputs, screenshots, and error traces. At runtime, gate-conditioned execution lets each step run code when its gates validate, or fall back locally when drift invalidates the executable realization. On WebArena-Verified, this http URL achieves 53.7% single-round success, improving over the strongest baseline by 3.9 percentage points. Across three re-executions, it retains 91.7% of initially successful tasks, 15.5 points above the next best method. Under bounded repair, it recovers 72.9% of subsequent failures while limiting post-repair regressions to 4.2%, compared with 15.0% to 17.0% for persistent baselines. It also leads on Mind2Web cross-website and cross-domain splits. In a GitLab migration test, this http URL preserves performance when reusing frozen state learned on GitLab 15.7, with frozen-versus-fresh target-version gaps of -1.7 points on GitLab 16.11 and +0.6 points on GitLab 18.9. These results identify lifecycle governance and gate-conditioned execution as reliability axes beyond one-shot task success.

Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)

arXiv:2606.08049 [cs.AI]

(or arXiv:2606.08049v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.08049 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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