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

2026-07-23

AI Summary

This work addresses the silent degradation of AI agents developed by non-engineering users on low-code/no-code platforms, which often occurs post-deployment due to changes in model versions, tooling, or permission dependencies, leading to a lack of sustained reliability. To tackle this challenge in democratized AI development, the paper proposes the first lightweight continuous assurance framework that embeds reliability guarantees throughout the agent lifecycle. The framework integrates dependency modeling, readiness contracts, automated scheduled checks, diagnostic reasoning, and lifecycle governance. A prototype auditor built upon this approach effectively generates actionable degradation alerts and repair recommendations. Scenario-based evaluations demonstrate the practicality and effectiveness of the proposed method in real-world deployment contexts.

Abstract

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.

Problem

• AI agent reliability
• democratization of AI
• silent degradation
• dependency management
• continuous assurance

Innovation

• continuous assurance
• AI agent reliability
• dependency mapping
• readiness contracts
• citizen development