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Toward Continuous Assurance for the Democratization of AI Agent Creation in Industry

Citation. Levy, N. & Berger, H. (2026). Toward Continuous Assurance for the Democratization of AI Agent Creation in Industry. arXiv preprint arXiv:2607.21495.

Summary. This preprint argues that non-engineering users can create organizational AI agents whose models, tools, retrieval sources, permissions, prompts, schedules, and external services may change after deployment. It proposes a continuous-assurance framework that translates these dependencies into readiness contracts, scheduled checks, diagnostics, and lifecycle governance. A hosted-GPT prototype is assessed in six author-defined scenarios as an initial feasibility demonstration rather than an effectiveness evaluation.

Key claims

  • Democratized organizational agents can silently degrade when their technical dependencies or ownership arrangements change even if no user directly edits the agent.
  • The proposed framework pairs dependency mapping with readiness contracts, periodic or change-triggered checks, failure diagnostics, and assigned ownership and escalation paths.
  • The prototype’s six scenario-based assessments produced readiness decisions consistent with the authors’ expected failure classes and available evidence, but do not estimate coverage, false-positive rates, or operational effectiveness.
  • The paper argues that uninspectable configurations, permissions, and retrieval indexes should be reported as unknown rather than inferred to be ready.

Evidence & limitations

  • The paper presents a conceptual taxonomy and framework plus a hosted-GPT prototype assessed against six author-defined fault scenarios; it is not a field study or an independent benchmark.
  • Its scenarios and expected findings were author-defined, and the authors identify missing evidence on detection coverage, false positives, time to recovery, and operational effectiveness.
  • This is an arXiv preprint (v1 dated 2026-07-23), not peer-reviewed evidence.

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