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 created by the democratization of AI agent creation within organizations. To address this, it proposes a lightweight continuous-assurance framework that includes dependency mapping, readiness contracts, scheduled checks, diagnostics, and lifecycle governance.

What the Document Says

• AI agents are being increasingly created by non-engineering users within organizations using low-code, no-code, and conversational development environments.
• This democratization of AI agent creation allows for rapid local innovation but also creates a reliability gap.
• The reliability gap is due to changing models, tools, retrieval sources, permissions, prompts, schedules, and external services that can cause silent degradation in agents even after deployment.
• A lightweight continuous-assurance framework is proposed for citizen-created organizational agents.
• This framework includes dependency mapping, readiness contracts, scheduled checks, diagnostics, and lifecycle governance to assess operational readiness under expected conditions.
• An initial prototype auditor and scenario-based assessment are presented to demonstrate the practical application of the proposed taxonomy.

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

• Democratization of AI agent creation within organizations can lead to a reliability gap.
• The proposed continuous-assurance framework can assess operational readiness under expected conditions.

Evidence Used

• AI agents are increasingly created inside organizations by non-engineering users through low-code, no-code, and conversational development environments.

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.

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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.