Inteq's Agentic AI Q&A Series
Question: How reliable does your data need to be for AI agents, and how do you know when it is good enough?
Answer: Your data needs to be as reliable as the stakes of the decision it feeds. There is no single universal threshold, and applying one uniformly wastes effort. Data driving a high-consequence or irreversible action demands higher rigor than data informing a low-stakes, easily reversed step.
That makes "good enough" a per-use-case definition. The data is reliable enough when the residual error rate is acceptable given the consequence of acting on it and the monitoring you have in place to catch problems. I advise leaders to define that threshold explicitly for each agent decision, rather than chasing an abstract notion of perfect data.
The payoff of this approach is that it concentrates remediation where an error is actually costly, instead of spreading effort thin across everything. It converts an open-ended, unfundable data-quality program into a targeted, justifiable investment tied to specific agent decisions - which is the only version of data readiness that survives a budget conversation.
Defining the data and knowledge an agent must access, and to what standard, is part of the requirements work taught in Inteq’s Analyzing & Specifying AI Agent Business Requirements training course.
