Industrial data-quality detection has become increasingly automated. Range checks, statistical methods and machine-learning models can now identify many issues across large time-series data estates. But what happens after detection has still been a time-consuming and mostly manual process. Up until now.
Traditionally, an engineer still needed to decide whether an anomaly is a data-quality issue or a legitimate process behavior. They still needed to examine related signals, establish the likely source and decide what action is required. At scale, the investigation could become a larger problem than the initial detection!
Agentic data quality (Agentic DQ) extends the automation of the monitoring model into that investigation. Rather than adding another layer of alerts or visualization, an agent can gather context, correlate evidence, develop hypotheses and support remediation while keeping the engineer responsible for decisions that require human judgment.
DETECTION
AutomatedINVESTIGATION
Engineer and agent- Decide whether the condition is a data-quality issue or legitimate process behavior
- Examine related signals
- Establish the likely source
- Assess what other data may be affected
- Determine what action is required
REMEDIATION
EngineerThe engineer decides what to change and confirms the result.
What an industrial data-quality agent actually does
1. Triages and prioritizes data-quality issues
An industrial data-quality agent can start from anomalies identified by the underlying data-quality system and determine which warrant attention first. Rather than presenting engineers with just a list of issues, it can consider factors such as severity, duration, recurrence, affected assets and the number of related signals involved.
This gives engineeris and data teams a prioritized view of where investigation is most valuable, while lower-impact or already understood conditions can be deprioritized.
2. Maintains a “bigger-picture” view of data health
APERIO’s proprietary Data Quality Index (DQI) provides an ongoing measure of data health across individual signals, equipment groups, process areas and the wider data estate. This allows teams to see where quality is deteriorating, identify recurring problem areas and determine whether remediation is producing sustained improvement.
For an agent, this provides a longer-term view that individual alerts cannot. It can identify patterns such as gradual deterioration across an asset, recurring quality problems within a process area or improvements following remediation, helping teams move from managing individual incidents to understanding the overall health of their operational data.
SUDDEN CHANGE AFTER A CONFIGURATION EVENT
DQI over several weeks
Investigate first: what changed at the time of the event, such as configuration, scaling or the source system.
GRADUAL DETERIORATION
DQI over several months
Investigate first: causes that develop slowly, such as instrument degradation or slow drift.
3. Investigates context and develops root-cause hypotheses
Once an issue warrants investigation, the agent can bring together the evidence needed to determine what is most likely happening. It can examine equipment state, related process variables, redundant measurements, communication status, historical behavior and previous incidents, then compare possible explanations against that evidence.
If a flow measurement flatlines while related process variables and a redundant measurement continue to change, for example, the evidence points more strongly toward an instrumentation or data-collection problem than a genuinely stable process condition. The agent can present the most likely causes, show the evidence supporting them and identify what the engineer should check next.
4. Tracks remediation and retains investigation history
The agent’s role does not end once a likely cause has been identified. It can maintain the record of the issue through remediation: what was detected, what the investigation concluded, what corrective action was taken and whether data quality subsequently improved.
That creates a closed-loop workflow:
Detect → Investigate → Diagnose → Remediate. When a similar condition occurs again, the agent can draw on previous investigations and remediation outcomes rather than requiring the engineer to start again from first principles. Over time, this creates a reusable body of operational data-quality knowledge.
Access, context and control
An Agentic DQ system needs access to enough operational context to investigate an issue effectively, but that should never require permission to modify the underlying data or systems. Read-only access can allow the agent to examine time-series data, related signals, asset context, configuration information and previous investigations while preserving existing operational controls.
The agent can then bring that evidence together, develop a likely cause and recommend the next course of action, while remediation remains subject to the organization’s existing permissions, workflows and human approval.
Agentic DQ in industrial AI workflows
Another powerful use-case for agentic DQ is when operational data move beyond the historian into cloud platforms, analytical environments and AI workflows.
For teams developing industrial AI, it’s often neccessary to establish not only which data was used, but what quality conditions existed, what remediation or transformations were applied and whether an identified issue remained unresolved during a specific period (for example a training period for an AI model).
Agentic DQ can maintain that context continuously, making data readiness an ongoing property of the data pipeline rather than a one-time assessment before model development or deployment.
Where APERIO fits
APERIO brings this approach to industrial data quality through Albert, its Agentic DQ solution. Albert builds on APERIO’s powerful data-quality detection and analysis capabilities to investigate detected conditions, correlate related signals and operational context, develop root-cause hypotheses and support remediation. These investigations can be initiated on demand for a specific issue or scheduled to run automatically on a recurring basis. Through a conversational interface, engineers can interrogate data-quality issues and the evidence behind them without starting each investigation from scratch.
To see how Albert can help your team move from data-quality monitoring to Agentic DQ, book a demonstration with APERIO.
From detection to investigation
Dashboards, validation rules and scalable anomaly detection remain important components of industrial data quality. Agentic DQ does not replace them; it changes what happens next.
Rules identify known conditions. Behavioral models identify unexpected patterns. An agent brings the available evidence together, investigates likely causes and maintains long-term context. The result is a shift from simply finding more data-quality issues to resolving them more efficiently.
For data teams, engineering leaders and CIOs, the opportunity is significant: less time spent manually investigating data-quality issues, faster resolution and a clearer, more continuous view of data health across the organization. That is the real potential of Agentic DQ: closing the distance between detecting a problem, understanding its cause and taking the right action.
The next step is to identify where that gap exists in your own data-quality workflow, and where an agent could remove the most manual effort. Talk to APERIO about how Agentic DQ and Albert could work in your industrial data environment.
Frequently Asked Questions:
Agentic Data Quality (Agentic DQ) applies AI agents to the data-quality workflow. Rather than only detecting and displaying data-quality issues, an agent can investigate anomalies, correlate related signals and operational context, develop root-cause hypotheses, and support remediation. In industrial environments, this moves data quality from detect and display toward detect, investigate, explain and remediate.
Traditional data quality monitoring typically uses rules, statistical methods or machine-learning models to detect conditions such as missing data, flatlines, out-of-range values or anomalous behavior. Agentic DQ extends this by investigating what caused the condition, examining related data and previous incidents, and maintaining context as the issue moves through remediation and verification.
AI agents can reduce the manual investigation required after a data-quality issue is detected. An agent can examine related process measurements, redundant instrumentation, historical behavior, quality indicators and previous incidents to determine which explanations are consistent with the available evidence. Engineers can then review the findings and decide what action is appropriate.
Agentic DQ can support root-cause analysis by correlating multiple observations and developing hypotheses about the likely source of an issue. For example, it can help distinguish between instrument degradation, calibration drift, communications problems and legitimate changes in process behavior. The system should expose the evidence supporting those hypotheses rather than treating an inferred cause as established fact.
No. Agentic DQ builds on existing detection methods rather than replacing them. Deterministic rules remain useful for known constraints, while machine-learning models can identify behavioral changes that are difficult to represent with static thresholds. The agent uses these detections as inputs to a broader investigation.
An industrial data-quality agent benefits from both time-series data and the context surrounding it. This can include source quality flags, engineering units, historical signal behavior, relationships between process variables and redundant instruments, previous data-quality incidents, and remediation history. The available context determines how far the agent can progress from anomaly detection toward meaningful diagnosis.
Agentic DQ can provide continuous visibility into the quality history of the OT data used by AI and machine-learning applications. This helps teams identify unresolved quality conditions, understand changes in signal behavior, and determine what remediation or transformation has been applied to the underlying data. Data readiness can therefore be managed continuously rather than assessed only before model development or deployment.
Albert is APERIO’s Agentic DQ solution for industrial data. It builds on APERIO’s industrial data-quality capabilities to help investigate detected conditions, correlate related signals and operational context, develop root-cause hypotheses and support remediation. Engineers can use its conversational interface to interrogate data-quality issues and the evidence behind them.