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 authors propose a lightweight continuous-assurance framework for ensuring reliability of AI agents created by non-engineering users within organizations through low-code, no-code, or conversational development environments.

What the Document Says

• AI agents are being created inside organizations by non-engineering users using low-code, no-code, and conversational development environments.
• This democratization of AI agent creation allows for rapid local innovation but creates a reliability gap as these agents can depend on various factors that may cause silent degradation post-deployment.
• The authors propose a continuous-assurance framework for citizen-created organizational agents that combines dependency mapping, readiness contracts, scheduled checks, diagnostics, and lifecycle governance.
• An initial prototype auditor and scenario-based assessment are presented to demonstrate the practicality of the proposed taxonomy.
• The goal is to assess whether an agent remains operationally ready under expected conditions.

Key Concepts Mentioned

• AI agents
• democratization
• reliability gap
• low-code
• no-code
• continuous-assurance framework
• dependency mapping
• readiness contracts
• scheduled checks
• diagnostics
• lifecycle governance

Claims Made by the Document

• Democratized AI agent creation allows for rapid local innovation but creates a reliability gap.

Evidence Used

• The authors argue that the democratization of AI agent creation enables rapid local innovation, but it also creates a reliability gap due to changing models, tools, permissions, prompts, schedules, and external services.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

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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.