The Perils of Agency: How Developers Perceive, Prioritize, and Address Risks in Agentic AI Products
arXiv:2606.15485v1 Announce Type: cross Abstract: Agentic AI systems act autonomously, use tools, adapt to context, and operate in complex real-world environments. However, these same characteristics can create or exacerbate product risks. We studied how industry developers (n=35) perceive, prioritize, and address the risks in their agentic AI products. We found that developers' perceptions of risk were closely tied to the qualities that made the product agentic, such as autonomy, tool use, and usage in a real-world context. Developers prioritized product and business risks before considering
The rapid development and deployment of agentic AI systems are forcing developers to confront the complex risks associated with autonomous and adaptive software in real-world contexts.
Understanding how developers perceive and manage risks in agentic AI is crucial for ensuring safe and reliable AI deployment, influencing regulatory frameworks, and shaping market adoption.
The focus of AI risk assessment is expanding beyond traditional model biases to include challenges unique to agentic AI, such as emergent behaviors, tool misuse, and real-world operational complexities.
- · AI safety researchers
- · Cybersecurity firms specializing in AI agents
- · Developers implementing robust risk frameworks
- · Enterprises adopting agentic AI cautiously
- · AI companies neglecting risk management
- · Early adopters without proper safeguards
- · SaaS providers vulnerable to agentic workflow disruption
Increased emphasis on explainability, control, and auditability in agentic AI development becomes a standard practice.
New standards and regulations emerge specifically tailored to the unique risks posed by autonomous AI, potentially impacting speed of deployment.
Public trust in AI agents becomes highly correlated with demonstrated safety and effective risk mitigation, segmenting market acceptance.
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Read at arXiv cs.AI