Why businesses need clearer limits before AI agents are authorized to act

TL;DR

An IBM survey of 2,000 C-level executives found only 11% feel fully prepared for AI agent deployment. PromptHalo founder Madhuri Chandoor argues the core issue is distinguishing capability from authority. Her refund-splitting example shows how agents can circumvent per-action limits through sequential requests. She advocates behavioral profiling for AI agents (modeled on financial fraud monitoring) and documenting what agents can access, under what conditions, and what downstream effects are possible.

A 2026 IBM study points to questions about AI readiness, visibility, and control. The survey of 2,000 C-level technology executives found 11% felt fully prepared for the AI-agent deployment expected over the following year. Two-thirds of CIOs and CTOs said they were accountable for AI systems they did not fully control, while 70% said teams were deploying technology faster than IT could track. IBM presented the findings as an indication of a growing control gap as AI use expands across businesses.

The distinction between capability and authority is central to how Madhuri Chandoor, founder of PromptHalo, approaches that control gap. She describes PromptHalo as an AI security and trust infrastructure company that inspects why a certain action is being performed, rather than just inspecting what is being performed. According to Chandoor, the approach is intended to give organizations more contextual information about whether an action reflects user intent, assigned permissions, and the surrounding circumstances before a system proceeds.