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

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Natan Levy, Harel Berger
• Venue: arXiv: cs.AI
• Published: 2026-07-23
• Collected: 2026-07-24
• URL: http://arxiv.org/abs/2607.21495v1
• PDF: https://arxiv.org/pdf/2607.21495v1
• DOI: UNKNOWN

Plain-Language Brief

The paper discusses a reliability challenge in AI agents created by non-engineering users within organizations, due to multiple dependencies that can cause silent degradation after deployment. The authors propose a lightweight continuous-assurance framework for ensuring the operational readiness of these citizen-created organizational agents.

What the Document Says

• AI agents are being created by non-engineering users within organizations through low-code, no-code, and conversational development environments.
• This democratization allows for rapid local innovation but also creates a reliability gap.
• The reliability issue arises from dependencies on changing models, tools, retrieval sources, permissions, prompts, schedules, and external services.
• These dependencies can cause silent degradation in agents even after deployment, without any direct user modifications.
• The paper proposes a lightweight continuous-assurance framework for citizen-created organizational agents.
• The framework includes dependency mapping, readiness contracts, scheduled checks, diagnostics, and lifecycle governance.
• The authors present an initial prototype auditor and scenario-based assessment showing how the proposed taxonomy can be translated into practical checks and remediation guidance.

Key Concepts Mentioned

• AI agents
• democratization
• reliability gap
• dependencies
• silent degradation
• continuous-assurance framework

Claims Made by the Document

• The democratization of AI agent creation enables rapid local innovation but also creates a reliability gap.
• Dependencies in AI agents can cause silent degradation after deployment.

Evidence Used

• The text mentions the increasing use of AI agents by non-engineering users, the potential for silent degradation, and the proposed continuous-assurance framework.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

AI agents are increasingly created inside organizations by non-engineering users through low-code, no-code, and conversational development environments. This democratization enables rapid local innovation, but it also creates a reliability gap: agents that appear to users as simple productivity artifacts may depend on changing models, tools, retrieval sources, permissions, prompts, schedules, and external services. These dependencies can cause silent degradation long after deployment, even when no user directly modifies the agent. This paper identifies the reliability challenge created by democratized AI agent creation and proposes a lightweight continuous-assurance framework for citizen-created organizational agents. The framework combines dependency mapping, readiness contracts, scheduled checks, diagnostics, and lifecycle governance to assess whether an agent remains operationally ready under expected conditions. We also present an initial prototype auditor and scenario-based assessment showing how the proposed taxonomy can be translated into practical checks and actionable remediation guidance.