Executives are spending a great deal of time asking whether AI agents are ready for production, but this is the wrong place to start, because agents aren’t acting in a vacuum. The quality of their decisions depends heavily on the quality of the data they use.
Model security and performance matter, of course, but they cannot compensate for weak control over the information the agent can retrieve. Before putting an agent into production, an organization needs governed inputs, appropriate access and a reliable record of what the agent produced.
CEO at Archive360.
Give an agent outdated customer information, duplicate records or material that was never properly classified, and it will work with what it has, making poorly informed decisions quickly across thousands of transactions.
Unfortunately, the gap between adoption and governance is widening fast. In the rush to adopt AI, many organizations are pushing agents into production before they have solved the underlying data problem, so it shouldn’t come as a surprise that the vast majority of AI projects show zero ROI.
What’s more, AI agent governance doesn’t end with the data an agent may access. It also encompasses the data an agent produces, such as its decisions, the instructions it was given and documents it created. As AI contributes to significant decisions within the enterprise, AI traceability and defensibility have become critical capabilities.
Organizations that build a strong data foundation put themselves in a stronger position to move from pilot to production with confidence that agents won’t transform poorly managed data into a company-wide crisis.
The data problem comes first
The data governance issue is not, of course, a new problem. Most large organizations have spent years trying to wrangle information spread across operational platforms, file shares, archives and applications that should have been retired long ago. But because agentic AI can rapidly act on those weaknesses at scale, the issue has become more pressing now.
Before an agent is given access to data, IT needs to identify sensitive and regulated information, apply classification and retention policies consistently, and determine which sources are current and reliable enough for the job. Legacy data deserves particular care.
Retired applications often contain valuable business history, but they also hold duplicate records, obsolete information and data that’s subject to legal holds or retention requirements.
Organizations should also preserve the context and relationships that give legacy data meaning, and then make selected, policy-controlled datasets available. AI does not act on data in a vacuum, and without the surrounding context, agents will either fail to complete their tasks or, worse, will make and act on faulty decisions.
Access is a business decision
Another mistake that enterprises make is to provide their agents with access to more data than they need. In fact, agents should also only have access to the data they require to complete the tasks they are assigned. Just because a system can be connected to an agent, that does not mean it should be.
Treat agents like employees, and follow the principle of least privilege. For example, while a customer service agent may need current account and transaction information, it most likely doesn’t need legal files or historic employee records. Access should narrowly follow the agent's defined purpose.
Provenance matters as well. An agent should not treat a current system of record and a decades-old archive as equally authoritative. Agents must know where information came from, when it was updated and which policies apply. Again, context matters. Without the proper context, agents will make costly mistakes.
Keep a record of what the agent did
Governance does not end once the agent receives approved data. AI-related data is rapidly becoming material to litigation, compliance reviews and customer complaints. To be prepared, an organization should be able to show what the agent was asked to do, which information it accessed, which policies were applied and what happened next.
Prompts, retrieved content, outputs and resulting decisions should be retained as business records. A reviewer should be able to reconstruct the AI’s decision and trace it back to both the source data and the person or function that authorized the activity.
Finally, accountability cannot belong to the technology team alone. Security, data, privacy, legal and compliance leaders all have a role, but a named business owner must remain responsible for the outcome. Automation can perform an action, but it cannot accept accountability for it.
Data readiness is not a secondary workstream to be addressed after an AI pilot succeeds. It is part of the decision to move that pilot into production. If an organization cannot trust the information, control access to it and clearly explain the resulting decisions, the agent is not ready for greater autonomy.
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