The AI Agent That Keeps SCADA Tags Honest

The situation

On a Fortune 200 renewable operator's SCADA environment, an AI agent runs in production doing an unglamorous but vital job: checking that the tag list still matches the live system. Sites change constantly - equipment is added, renamed, reconfigured - and the tag definitions that everything else depends on slowly fall out of step with reality. Usually nobody notices until a screen or a report shows something that is plainly wrong.

The defect

Tag drift is a silent failure. Every dashboard, alarm, and report is built on the assumption that a tag still points at what its name says - and when that quietly stops being true, the whole stack above it is confidently wrong. The old fix was a one-time cleanup that started decaying the day it finished. What was missing was not another manual audit but a way to treat data integrity as something continuous, checked constantly rather than rediscovered after a mistake.

What we engineered

• Put an AI agent into production that continuously checks SCADA tags against the live system and surfaces mismatches.
• Secured it with basic authentication over SSL, appropriate to an operational SCADA environment.
• Validated it through disaster-recovery testing, so it holds up when the environment does not.
• Flagged drift as it happens, so a wrong tag is caught before it reaches a screen or a report.
• Made data integrity a running process rather than a periodic cleanup project.

The result

Tag accuracy went from something checked occasionally to something watched continuously. The agent catches mismatches as sites change, so the dashboards and reports built on those tags stay trustworthy instead of drifting until a person spots the error. Running in production and DR-tested, it reflects how SCADADOG thinks about data integrity: not a one-time cleanup, but a process that keeps the data honest over time.