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

arXiv: https://arxiv.org/abs/2607.21495 · HTML: https://mlsanity.com/paper/2607.21495/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 8 references that are in the map.
- First posted on arXiv 23 Jul 2026.

## Builds on (most cited first, 6 of 6)

- [AgentOps: Enabling Observability of LLM Agents](https://mlsanity.com/paper/2411.05285/) (2024)
- [Internet of Agents: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2505.07176/) (2025)
- [The Adoption and Usage of AI Agents: Early Evidence from Perplexity](https://mlsanity.com/paper/2512.07828/) (2025)
- [Problem-Space Evasion Attacks in the Android OS: a Survey](https://mlsanity.com/paper/2205.14576/) (2022)
- [Can you See me? On the Visibility of NOPs against Android Malware Detectors](https://mlsanity.com/paper/2312.17356/) (2023)
- [A Large-Scale Empirical Analysis of Custom GPTs' Vulnerabilities in the OpenAI Ecosystem](https://mlsanity.com/paper/2505.08148/) (2025)

## Abstract (arXiv)

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.