The End of Data Remodeling? How to Develop AI Agents Faster, Cheaper, and More Securely 

ETL and other data remodeling projects are, quite possibly, the biggest blockers of AI agent development. According to BCG, 74% of organizations struggle to achieve value from their AI initiatives, only 4% have developed truly cross-functional AI capabilities, and 29% of AI agent projects missed deadlines in 2024. 

AI agents are only as good as the data they can access. Therefore, if you want to create useful AI agents, you need to give them easy, reliable, and fast access to trustworthy data.  

Unfortunately, organizations are not set up to give agents that access. Data is fragmented across departments and technologies, including many legacy technologies. That fragmentation makes it extremely difficult to find the latest information, know which version of a report is the most recent, or gather all the relevant data about a specific topic. Search for “Q4 Results” and you’re likely to get a mishmash of spreadsheets, reports, and maybe a few press releases—some of that information will probably pertain to the most recent Q4, and some to previous Q4s. But there’s no guarantee you’ll have all the data or even the most important data. 

The current response to data fragmentation is data centralization: moving every bit of data from wherever it currently sits into a vector database, data lake, or other master data repository. Maintaining data pipelines, syncing updates, and enforcing security across yet another layer? That’s a data remodeling project of epic scale, and as anyone who has ever done a major remodeling project can attest, the more you do the more there is to do. They do end—eventually—but only after eating up more time, money, and focus than you would have believed possible. 

Making the project more difficult, businesses can’t afford to shut down their data operations for months while they remodel. That means they have to remodel while keeping everything working (it’s hard enough living in your house during a remodel and this is more like rebuilding an airplane in flight!).  

That’s a big lift just to start AI agent development. And if, in the end, the agent doesn’t work out? Maybe you’ll be able to use all that new infrastructure on a different project, maybe it will all have been for naught. The costs of experimentation and learning are very high and undoing it all is equally difficult. And that’s a problem because who wants to embark on a risky project that may leave you worse off than when you started? 

Is Remodeling Really Necessary? 

Forget the vector database and any other data centralization claims. A Zero-ETL approach to agent development gives agents access to data without having to redesign your technology infrastructure just to get started. Zero-ETL reduces risk by leaving data in place, minimizing upfront costs and prep work, and avoiding major remodeling. It lets you start agent development sooner, significantly reduce the costs of failure, and focus on agent development, not on tuning ETL pipelines. 

Zero-ETL is also more secure than data centralization. Rather than create an attractive attack target, with yet another security protocol to manage, many Zero-ETL solutions can integrate into your existing security. Integration also makes it easier to manage security for your agents: keeping the technological infrastructure less complex means there are fewer opportunities for errors or vulnerabilities.  

Another benefit of Zero-ETL? You eliminate the risk of having to update your brand-new vector database halfway through your remodeling project or replace it with yet another magical database technology when the current one becomes obsolete.  

Zero-ETL AI Search Enables Agents  

AI Search, built with a Zero-ETL approach, is how we put an end to data remodeling projects and give agents the data they need to carry out our instructions. Middleware, such as SWIRL AI Search, sits between AI and our data, providing universal connectivity and intelligent search capabilities.  

SWIRL connects to over 100 (and counting) applications and simultaneously searches all connected sources in response to a query. Queries can be human or agent generated. SWIRL uses AI to organize, deduplicate, and relevancy rank the results of the search. 

SWIRL integrates with existing security, so your existing enterprise security, privacy, and access policies are automatically applied. Users and agents can only search the data locations that they are allowed to access and cannot see what they are not allowed to access. As autonomous agents proliferate, manually updating security policies in multiple locations is a recipe for trouble; with SWIRL, such manual updating isn’t necessary. 

Because SWIRL leaves data in place and manages all communication with the AI, the AI only sees the data that is relevant to the question at hand. AI access to data is strictly controlled, limiting the potential for AI data leakage: There is simply no need to dump all your data into an AI. SWIRL is also AI agnostic, so you can use whichever models best meet the needs of your organization. 

Avoiding data consolidation means that data is kept under your control, your data is not centralized into one big juicy target, and AI projects can launch without extensive data remodeling. These factors sharply reduce risk and make rollback easy. SWIRL seamlessly becomes part of your data ecosystem but doesn’t require you to redesign your world to make SWIRL work. And, unlike remodeling projects, SWIRL can be up and running in days, not months.  

Forget the Remodel! 

There is no question that AI agents need access to data to be worthwhile. The only question is how we give them that access: do we engage in a massive remodeling effort to centralize our data, hoping that it will give agents the access they need (while also hoping that upgrades to the destination database won’t undo our work)? Or do we go with a solution that leaves data in place, avoids the time and risks of remodeling, and lets us focus our efforts on agent development, not data management and technology upgrades? 

Contact us to try SWIRL AI Search free for 30 days, and experience the power and convenience of easy, fast, reliable data access without the chaos of data remodeling.