AI agents need structured and governed data
Some AI agents simply follow predefined steps. Others explore, solve loosely defined problems or even create new agents. The more autonomy and intelligence we demand from AI agents, the more they depend on structured and meaningful data. Simply having data in a lake isn’t enough. Agents require data presented through clear and consistent interfaces with strictly defined access.
This is precisely the value of treating data as a product. Data products are curated datasets with defined schemas, clear interfaces, robust governance and lifecycle management. This structured approach allows AI agents to safely and effectively interact with data.
You don’t need to do everything at once. Start with a practical, high-value use case. Create focused data products specifically to support that use case. That’s how the value becomes tangible.
From discovery to safe execution
Many organisations already have decent metadata layers, describing data assets consistently. Some even leverage LLMs to assist humans with search and discovery.
That’s great for human exploration, but autonomous agents need more.
To effectively act on data, agents require:
- Reliable interfaces (such as APIs, SQL endpoints, structured files)
- Clear documentation and schemas (to interpret data correctly)
- Defined policies and guardrails (to control permitted actions and prevent misuse)
If we stop at metadata alone, we’re effectively giving agents a directory without keys, or worse, unrestricted keys with no oversight.
Should AI agents talk directly to source systems?
Sometimes, yes. If your source systems offer clean APIs and robust security, direct access might be fine.
But often, data needs cleaning, enrichment and consolidation before it’s usable by agents. Data products bridge the gap between raw complexity and agentic AI, offering structured, clean and governed interfaces.
Equally critical are usage rules: which agents can access the data, what operations they’re permitted and how sensitive information is managed. Data products encapsulate both data and context, significantly reducing risk.
Agents shouldn’t access everything freely
Agentic AI is powerful, but power without guardrails introduces significant risks. Imagine a scenario where an unsupervised agent mistakenly triggers production system downtime or accidentally exposes sensitive customer information. These are realistic, preventable risks.
Treat AI agents like junior team members. They require onboarding, supervision, defined scope and limited access. They aren’t mere scripts anymore, they’re entities capable of decisions and real-time actions.
This governance can’t be an afterthought, it must be integral to your data products from day one. This means:
- Clearly defining the scope and permissions for each agent
- Continuous monitoring of agent interactions
- Designing interfaces that inherently enforce these restrictions
Without governance, agentic AI poses risks. With appropriate control, it becomes a powerful, safe business tool.
Final thoughts and an invitation
Agentic AI won’t run effectively on one monolithic data lake. It will thrive within a structured network of governed data products, each designed with usability, clarity and safety at its core.
You don’t need a revolutionary overhaul to start. One targeted use case is sufficient. Identify necessary data, package it as products, enforce access rules and let your AI agents perform.
That’s how we operate. If you’re considering agentic AI, we’d love to help you identify the best starting point and turn it into reality. Read more about our data-driven services.
What’s next? In the next blog around this topic, we’ll flip the perspective and explore how agentic AI can itself automate the creation of data products and accelerate your journey to becoming truly data-driven.