Data Quality: The Hidden Foundation of Effective AI Agents

When an AI agent makes a poor decision, the instinct is to blame the model or the workflow design. But more often than not, the real culprit is data quality. Agents reason over data; if the data is incomplete, inconsistent or wrong, the agent’s outputs will be too.

Data quality is the hidden foundation of effective agentic workflows. Here is how to get it right.

Why data quality matters more for agents

Traditional automation follows fixed rules. If the data is bad, the automation produces bad outputs - but at least the behaviour is predictable.

AI agents are different. They interpret, reason and adapt. Bad data does not just produce bad outputs; it can lead to unpredictable behaviour that is hard to diagnose.

For example:

• An agent routing customer enquiries may misclassify a complaint if the category field is unreliable.
• An agent drafting responses may hallucinate details if the source knowledge base is out of date.
• An agent prioritising leads may make poor recommendations if CRM data is inconsistent.

The flexibility that makes agents powerful also makes them vulnerable to data problems.

Common data quality issues

Data quality problems come in many forms. The most common in the context of agentic workflows include:

Incompleteness

Missing fields, empty records and gaps in history. Agents often need complete context to make good decisions; missing data forces them to guess or escalate unnecessarily.

Inconsistency

The same information recorded differently in different systems. A customer’s name spelled three ways, dates in multiple formats, or conflicting status fields. Agents struggle to reconcile conflicting data.

Staleness

Data that was accurate once but is now out of date. Knowledge bases that have not been refreshed, contact details that have changed, or policies that have been superseded. Agents working with stale data give stale answers.

Inaccuracy

Data that was never correct. Typos, data entry errors, or deliberate misinformation. Agents cannot distinguish accurate data from inaccurate data; they treat everything as true.

Duplication

The same entity recorded multiple times. Duplicate customer records, duplicate transactions, duplicate documents. Agents may process the same thing twice or produce inconsistent outputs.

Assessing your data readiness

Before deploying an agentic workflow, assess the data it will rely on. Ask:

• What data sources will the agent use?
• How complete is the data? What fields are often missing?
• How consistent is the data across systems?
• When was the data last updated? Is there a refresh process?
• How was the data validated? What error rates are known?

This assessment should involve both technical teams and the people who work with the data daily. Surface-level metrics can mask problems that users know about but have not reported.

Fixing data quality issues

Fixing data quality is not glamorous, but it is essential. Approaches include:

Data cleansing

One-time efforts to correct known errors, fill gaps and remove duplicates. This can be done manually for small datasets or with automated tools for larger ones.

Validation at entry

Preventing bad data from entering systems in the first place. Required fields, format validation, and real-time checks against reference data all help.

Ongoing monitoring

Data quality degrades over time. Implement dashboards and alerts that track key quality metrics and flag regressions before they affect agent performance.

Master data management

For organisations with many systems, a master data management approach can help ensure consistency. This involves designating authoritative sources for key entities and synchronising data across systems.

The agent feedback loop

AI agents can actually help improve data quality. When an agent encounters missing, inconsistent or apparently incorrect data, it can flag the issue for human review.

Design your workflows to capture these signals:

• Log instances where the agent lacked sufficient data.
• Track cases where agent outputs were corrected by humans.
• Surface patterns that suggest systemic data problems.

Over time, this feedback loop turns your agents into data quality sensors, surfacing issues that would otherwise remain hidden.

The cost of ignoring data quality

Organisations that skip data quality work pay the price later. Agents underperform, users lose trust, and projects stall. The time saved by cutting corners is dwarfed by the time spent troubleshooting and rebuilding confidence.

Invest in data quality upfront. It is less exciting than building new capabilities, but it is the foundation on which everything else depends.