The Future is Agentic

Beyond Automation,
Toward Autonomous Data Agents.

AI is making inroads into data management, but we’re on the cusp of something even bigger: autonomous AI agents transforming how data teams operate. Unlike traditional scripted automation (which only handles predefined repetitive tasks), AI agents bring a new level of intelligence and decision-making to the table.

They can analyze data, make choices, and even collaborate with other AI models, essentially functioning with a degree of “agency” rather than just following rigid rules. This shift promises unique advantages that extend beyond mere efficiency, heralding a future of data management that is truly agentic.

Your 24/7 Virtual Data Team.

One of the most exciting aspects of AI agents is that they act like an always-on extension of your data team. These virtual agents will work tirelessly 24/7, constantly monitoring and maintaining data quality across all your systems. They don’t just flag issues; they proactively identify and fix data problems as they arise, from catching duplicate records to correcting anomalies, without waiting for a human to intervene.

In effect, it’s like having a full data operations crew working round the clock at no extra cost – a whole virtual data team keeping your data clean and up-to-date at all times. By handling these routine but crucial tasks, AI agents free up your human experts to focus on higher-value activities instead of firefighting data quality issues.

[Video - Introducing CluedIn's Virtual AI Agents.]

Humans in the Loop for Critical Decisions.

Crucially, embracing AI agents doesn’t mean removing humans from the equation. In fact, the most successful deployments treat AI agents as collaborators and assistants rather than replacements. These agents handle the grunt work under the guidance of human team members, they aren’t displacing human oversight.

For example, an AI agent might sift through millions of records to find discrepancies or suggest merge candidates for duplicate entries. It will do the heavy lifting of analysis and even propose fixes, but a data steward (a human) will review the flagged items and approve any changes before they’re applied.

This human-in-the-loop approach ensures that expert judgment remains in charge of critical decisions. The AI takes care of the tedious workload, while humans provide direction, make the nuanced calls, and handle exceptions. The result is a partnership where human expertise shapes the agent’s actions, enhancing both productivity and confidence in the process.

In short, your data team gets to focus on strategic initiatives, trusting the AI to handle the busywork with oversight in place when it counts.

Traceability and Trust.
Every Change Accounted For.

When AI agents are operating on your data, tracking their “thinking” and changes is vital. In the realm of data governance, companies require consistent, transparent, and auditable processes – and AI-driven operations are no exception.

The good news is that modern AI agents introduced here at CluedIn, are being designed with traceability in mind. They log the actions they take and can provide a clear lineage of data transformations. In fact, CluedIn agents are moving from trial-and-error heuristics to more deterministic, rule-based approaches once they learn what works best, specifically to meet compliance needs.

For instance, a CluedIn AI agent might experiment with various ways to standardize an address field; once it discovers the optimal method, it will formalize that as a rule and apply it consistently going forward, it will even provide you it's thinking and rationale.

Every change is thus repeatable and auditable. This means that at any point, your team (or an auditor) can trace what the AI agent did, why it did it, and what data was affected, ensuring transparency and accountability for every AI-driven action. By turning learned best practices into persistent rules, AI agents help maintain governance standards.

Companies can trust these AI-driven processes because they come with a built-in audit trail, providing the confidence that even as machines do the work, nothing important happens in a black box. It’s all visible, logged, and explainable, a critical factor in building trust in an AI-augmented data management strategy.

Conversational Copilots Make Data Management Effortless.

Another breakthrough accompanying AI agents is the rise of AI copilots – conversational assistants that make interacting with your data platform as easy as chatting with a colleague. Instead of clicking through complex UIs or writing scripts, users can simply tell the AI what they need in natural language.

CluedIn’s built-in AI Copilot is a prime example: you can “just type out a command in any language, hit Enter, and let CluedIn master your data instantly.”. In other words, no coding or technical expertise is required to get things done.

Want to merge two customer records, apply a standard format to all phone numbers, or generate a new data quality rule? Just ask the CluedIn Copilot, and it will execute the task in moments. This removes the traditional bottlenecks of needing specialized data engineers for every little change.

In fact, by leveraging powerful language models (through Azure OpenAI services under the hood), CluedIn’s AI Copilot can reduce manual work by a factor of 50:1 for many operations. Tasks that once took days or weeks of effort can be completed in minutes, or even seconds, by anyone on your team with basic computer skills.

The platform can automatically clean, standardize, and enrich data in a fraction of the time it used to take and even proactively suggest which business rules or data improvements you should implement next.

This is a game-changer for enterprise data teams: it democratizes data management, allowing analysts, stewards, or even business users to directly interact with the data and shape it to their needs, all through simple prompts. By combining a conversational interface with the heavy lifting done by AI agents in the background, CluedIn and similar platforms are making data management both easier and smarter than ever before.

Native Integration into Your Ecosystem

Enterprise data doesn’t live in one system, and neither do AI agents. A key strength of CluedIn’s agentic approach is its native integration with the broader data ecosystem. Because CluedIn is built on Microsoft Azure and designed to plug into existing tools, its AI agents and copilots can seamlessly interact with other platforms and services your business uses.

For example, these AI-driven processes can directly update your data catalog or governance tool – CluedIn’s Copilot has the ability to “interact with Azure products including Microsoft Purview and Fabric.” This means an AI agent can automatically push metadata or lineage information to Microsoft Purview (ensuring your data catalog is always up to date with the latest changes and quality metrics) and feed cleansed, mastered data into Microsoft Fabric for analytics or machine learning workloads.

The integrations don’t stop there: CluedIn also ties into tools like Power BI, Power Automate, Azure Data Factory, and many more, enabling a flow of information and actions across the entire Microsoft data stack.

In practical terms, once you’ve connected CluedIn to your data sources and defined your business rules, the AI agents can enforce those rules everywhere. If a new data policy is set, it’s immediately applied and can even trigger workflows via Power Automate or alerts in Purview for governance compliance.

The platform’s deep integration means automation extends beyond CluedIn itself, reaching into downstream and upstream systems. This unified approach ensures that your data management isn’t happening in a silo; it becomes an orchestrated part of your enterprise IT landscape.

The AI agents can truly “see” and act on the whole data supply chain, from ingestion to cataloging to analytics, making the entire pipeline smarter. For data teams, this removes a huge burden: instead of manually coordinating fixes and updates across multiple tools, you configure things once in CluedIn and let the connected AI ecosystem maintain alignment across all systems. It’s a point-and-click or plug-and-play style of automation that scales governance and quality everywhere your data lives.

Pioneering a “Point, Click, Connect – Cleanse and Maintain” Era.

All of these advancements point to a fundamentally new paradigm for enterprise data management. We’re entering an era where you can point to your data sources, click or connect them into your platform, and then let intelligent agents cleanse and maintain that data continuously. The heavy lifting of profiling data, spotting issues, correcting errors, and enforcing policies can be handled largely by AI that works in the background.

CluedIn is at the forefront of this movement, effectively pioneering this point-and-connect, then let AI do the rest approach to data management. Together with Microsoft, CluedIn has shown how proactive copilots and AI agents are making data governance smarter, easier, and more accessible to the business – delivering “ready-for-anything” data faster than ever.

This is not a distant future or theoretical vision; it’s happening now in leading-edge organizations. Data teams that adopt this agentic model find that their role shifts from reactive data janitors to proactive data curators and strategists. Rather than spending days fixing spreadsheets or chasing down quality issues, they can rely on the system to handle those tasks and instead focus on defining governance standards, refining business logic, and collaborating with stakeholders on using data to drive value.

For enterprise leaders like CIOs and CDOs, the implications are equally exciting. When much of the tedious, manual effort of data management is offloaded to AI, your teams can achieve more with less. By automating the toughest parts of maintaining data quality and compliance, organizations are saving significant time, money, and resources.

You get continuously clean, trustworthy data without constantly growing headcount or budget. More importantly, you gain agility, the ability to respond faster to new data needs or quality problems, because your virtual data team (the AI agents) is working on it immediately. And with humans still in the loop at critical control points, you maintain the oversight and governance needed to satisfy regulatory requirements and internal data standards.

Final thoughts...

In sum, the future is agentic: data management powered by AI agents and copilots means less grunt work and more intelligent orchestration. Enterprise data teams and business leaders alike can look forward to a world where data is always fit for purpose, curated by ever-vigilant AI assistants, and where unlocking insights is as simple as asking for them.

CluedIn’s example today gives a glimpse of what’s coming, a world of point, click, connect, cleanse, and maintain, where AI tirelessly handles the complexities of data management so that humans can concentrate on making data-driven decisions and innovations that drive the business forward.