Reltio Connect

MDM Teams - Turns Out You've Been Training for Agentic AI All Along

If you have spent any time on a data management team over the last couple of years, you have noticed something: suddenly everyone is talking about AI agents. Your executives are asking about them. Your vendors are pitching them. Articles educating you about them are everywhere, and somehow, they all seem to be written either for software engineers or for people who have never touched a database in their lives. This post is for neither of those groups. It is for the MDM practitioners — the data stewards, the architects, the project managers, the directors who have spent years making master data work in the real world. You do not need a computer science degree to understand what agentic AI is and why it matters to your work. You just need someone to translate it into terms you already know.

That is the goal of this blog.

Let’s start with what an AI agent actually is

In the most simple terms an AI agent is simply a software system that can perceive a situation, reason about it, decide what to do, and then act — often in a loop, repeating that cycle until a task is complete. Here is an analogy that might resonate: think about how a skilled data steward handles a duplicate record investigation. They look at the data (perceive). They consider the sources, the timestamps, the business rules (reason). They decide whether to merge, flag, or escalate (decide). Then they take action. And if the action creates a new situation — say, the merge surfaces a related issue — they loop back and start again. An AI agent does exactly that, just faster and at a scale no human team can match. The “agent” part means it is not just answering a question and stopping — it is actively working toward a goal.

The MDM vocabulary you already know maps directly

I had a conversation with a Customer recently, and we started using MDM and data unification concepts, to equate to the Agentic AI Equivalent, and it resonated with their team.  Here are a few direct parallels:

You are not starting from zero. You are starting from experience.

What makes an agent “agentic” vs. just “AI”

I have heard this question asked a lot. Traditional AI — the kind most of us have been using for a few years now — is reactive. You ask it a question, it gives you an answer. You paste in a data sample, it tells you something about it. Every interaction starts and ends with you. Agentic AI is proactive. You give it a goal, and it figures out the steps. It might search a database, then call an API, then write a record, then check the result, then decide whether to continue or escalate — all without you directing each move. You are setting the destination, not navigating every turn.

For MDM professionals, the practical implication is significant: an agentic system can handle workflows that used to require a human sitting at a keyboard walking through each step. Onboarding a new supplier. Resolving a customer duplicate across systems. Enriching a product record from multiple external sources. These are not hypothetical use cases — they are things forward-deployed teams like ours are building right now.

The one thing agents cannot do without you

One thing I think the majority of the population skips over that MDM practitioners know very well is, agents are only as good as the data they reason from. Full stop. A brilliant agent working from messy, untrustworthy master data, or context, will make confident wrong decisions, and will make them fast. This is not a small problem — it is the central problem. The organizations that will get the most out of agentic AI are the ones that have already done the hard work of getting their master data right. This means that every MDM project you have ever delivered — every golden record you’ve built, every survivorship rule you’ve argued for, every data quality standard you’ve enforced — was laying groundwork for this moment, even if you did not know it at the time. MDM, and all of the rich context that comes with it, is not a legacy discipline being replaced by AI. It is the foundation that makes AI trustworthy.

Three questions every MDM professional should be asking right now

You do not need to become a machine learning engineer to stay relevant in the agentic era. But there are three questions worth bringing into your next planning conversation: Where in our current data workflows are humans doing repetitive, rules-based decisions? Your data stewards know the answer to this immediately. These are the highest-probability candidates for agentic automation. Match and merge reviews, duplicate flagging, record enrichment approvals — look for the patterns. How confident are we in our master data quality? Not “good enough for reporting” confident. Confident enough that you would let a system act on it autonomously. That is a different standard. Who in our organization owns the governance of AI agent decisions? This is the data stewardship question for the agentic era. Agents will make decisions. Someone needs to own the review, the override policy, and the audit trail. Hint: MDM people are well-positioned for this role.

Find the value, not just the cost

One thing organizations beginning an AI transformation are struggling with in particular is identifying value. Many times the focus is on cost, and how much an agent may be costing the organization. Credit consumption is an important metric, but that should be balanced with the VALUE the agent is also realizing. Be sure to also have metrics that measure things like time to value, CSAT, and productivity. One thing I have started reinforcing with our own Customers is that you can keep the same metrics you had before an agentic solution was introduced - if your agentic solution is introducing more value, those metrics will improve.  

You already know more than you think

I have met a lot of MDM professionals through the years who have spent years fighting for data quality in organizations that sometimes did not fully appreciate why it mattered. They built the processes, the governance frameworks, and the institutional knowledge that makes trusted data possible. Agentic AI does not make that work irrelevant. It finally gives that work the stage it always deserved. You do not need a PhD. You need to keep doing what you’ve always done — and start asking how an agent could do the repetitive parts faster.