Enterprises are moving quickly to put AI agents to work. But building agents that can perform specific tasks is only part of the challenge. The harder question is whether the organization is prepared for those agents to act together on longer, multi-step workflows.
This is an important distinction because agents represent a fundamental shift from much of the enterprise AI that has existed to date. An AI system that generates an answer leaves the decision about what to do next largely in human hands. Agents can go further: they can interact with systems, and with each other, moving through workflows, and taking actions on behalf of an individual or organization.
Moving from answers to actions changes the risk equation.
For an agent to perform useful work, it needs access to the right information, contextual understanding of that information, and clear boundaries around what it can and cannot do.
That makes two foundations particularly important before agents go live: data readiness and governance.
Neither is a new challenge for enterprises, but agents raise the stakes for both.
The Data Behind the Agent
An agent can only be as effective as the information available to it. If the data it relies on is inaccurate, outdated, poorly labeled, or missing context, even a highly capable agent will likely not produce the desired outcome or value.
That does not mean organizations need to solve their entire enterprise data problem prior to deploying agents.
For the vast majority of companies, that would be unrealistic. Enterprise data has grown exponentially over decades across different systems, functions, and formats. Some exists in databases and tables; however, most of it lives in presentations, PDFs, emails, documents and other unstructured sources. Getting all that data into perfect condition can easily become a multi-year undertaking.
Fortunately, it doesn’t have to be the starting point.
Rather than asking, “Is all of our data ready for AI?”, organizations should ask, “Is the data required for this workflow ready for AI?”
Start with the work you want agents to perform and work backward.
Consider an organization looking to introduce agents into its PR/Communications function.
“Communications” is too broad a starting point. Is the goal to improve external comms, employee comms or crisis comms? Within one of those areas, which specific workflow is the agent expected to perform?
Once the workflow is defined, the data problem becomes much more manageable.
What information does each agent need? Where does it live? Is it current and accurate? Does it contain the context necessary to interpret it correctly? And is the agent permitted to use it for this purpose?
This is what data readiness looks like in the context of agents. Rather than exhausting resources chasing AI-readiness across your entire enterprise, focus on ensuring that the information required for a particular job is trusted, understandable, and appropriate for use.
But having the right information is only half the equation; determining what the agent is allowed to do is just as important.
Setting Proper Guardrails
Operational governance is essential in establishing, monitoring, and auditing what information an agent acts on.
Most enterprises already have rules governing what information employees can access. Agents require a similar framework, but with an additional dimension: organizations must also govern what an agent can do.
Can the agent read a customer record? Can it change that record? Can it initiate a payment? Send an external email? Approve a transaction? Can it recommend an action but require a person to authorize it?
These distinctions become critical as agents are given more autonomy.
An agent’s access and authority should be appropriate to the work they have been assigned, and no broader than necessary to perform it. Importantly, those boundaries cannot exist only in an AI policy document. They must be embedded in the systems and workflows through which agents operate.
If an agent shouldn’t have access to certain customer information, the technology should prevent that access. If it shouldn’t be able to send an external communication without approval, the workflow should enforce that approval. If a transaction above a certain threshold requires human authorization, the agent should not have the ability to bypass it.
The goal is to create the conditions under which organizations can give agents greater responsibility and autonomy with confidence by providing clear guardrails for where each agent’s permissions begin and end.
This also requires organizations to think more broadly about where AI risk resides.
An agent does not operate in isolation. It sits within a network of enterprise data, applications, APIs, identity systems, permissions, and workflows. As its authority grows, so does the potential consequence of an error, both internally and externally.
Organizations need visibility into the system as a whole to manage risk and accountability. It should be clear what information the agent accessed, what instructions and context informed its decision, what action it took, which systems it interacted with, and where human approval entered the process.
Auditability matters when something goes wrong, but it is equally important when things go right. It allows organizations to understand how agents are operating, and identify opportunities for improvement and optimization.
This is where data readiness and governance ultimately come together.
Data readiness determines whether an agent has the information required to do its job. Governance determines the authority it has to act on that information. And auditability ensures the organization can understand what happened afterward.
The enterprises that make meaningful progress with agents will be the ones that begin with well-defined workflows, prepare the data those workflows require, and put controls around the authority they give their agents.
Getting AI agents ready for real work requires the right foundations. Talk to our team about moving from experimentation to responsible deployment.
