Over the past few years, organisations have invested heavily in making their data AI-ready. Data lakes have been established, data sources cleaned up, governance strengthened and data quality improved. This has created an important foundation for AI applications that analyse data, identify patterns and make recommendations.
But Agentic AI raises the bar. An AI agent must not only understand information, but also be able to act on it autonomously. That requires more than just good data.
In this blog, we use a customer question about a delayed order to illustrate the difference between AI-ready and agent-ready data, the four building blocks required, and how you can take the first steps towards agent-ready data .
What is the difference between AI-ready and agent-ready data?
Consider a customer who sends an email asking about a delayed order.
| With AI-ready data | With agent-ready data |
| Recognise sentiment | Understand the customer’s question |
| Suggest a response | Check the order status |
| Create a summary for an employee | Consult the CRM |
| Retrieve transport information | |
| Update the case | |
| Send an update to the customer | |
| Escalate when necessary |
With AI-ready data , AI primarily supports analysis and advice. An agent with agent-ready data can also retrieve information, consult systems and perform actions within defined boundaries.
To do this, an agent needs not only accurate data, but also context, access to systems, business rules and clearly defined approval points.
Four building blocks for agent-ready data
1. Semantically rich data
An AI agent must not only be able to see data, but also understand what it means within the business context. A risk score, for example, is not simply a number. The agent needs to know which company the score relates to, how it should be interpreted and which next steps are associated with it.
Consistent definitions and reliable identification of customers, suppliers and other entities are essential.
Interesting read: AI experiment shows why reliable business data makes the difference
2. Real-time context
Agents act based on what is happening now. Relevant status changes, transactions, workflow events and other signals therefore need to be available in a timely manner.
A credit decision based on an outdated payment status can immediately lead to the wrong outcome. Agent-ready data therefore needs to be not only reliable, but also up to date.
3. Action-oriented architecture
Information alone is not enough. Data must also be connected to the actions that follow from it.
If a customer’s risk profile deteriorates, for example, an agent can retrieve additional information, initiate a reassessment, trigger a workflow or notify an employee. The transition from knowing to doing must be technically possible.
Interesting read: Agentic AI: from hype to practical reality
4. Governance at the source
The more autonomous agents become, the more important clear boundaries become. Which systems can an agent access? Which actions can it perform independently? And when is human approval required?
Authorisations, policies, compliance requirements and logging must therefore be an integral part of the process. Governance is not a control applied afterwards, but a prerequisite for every action.
Interesting read: Data provenance: trust in business data starts at the source
How do you make data agent-ready?
The transition to agent-ready data usually does not require an entirely new platform. Many organisations already have a strong foundation in place. The next step is primarily about connecting data, context and actions more effectively.
The following steps often deliver the most value:
- Harmonise definitions across systems.
- Ensure consistent identification of customers, products and transactions.
- Make business rules explicit.
- Make actions accessible through APIs.
- Enrich data with up-to-date events.
- Bring access rights and governance closer to the source.
This shifts the question from “Can AI understand our data?” to “Can AI act on that data safely, reliably and in a controlled way?”
Want to know how to prepare your data for AI agents? Our experts are happy to help you explore how reliable business data can support your AI applications.
