It is undisputed that AI brings massive productivity gains to slow, broken processes or the ones requiring a lot of manual labor like writing code. But what if your environment is already highly optimized? Can you still find a "next level" of efficiency?
I recently sat down with some data engineers to see how they are driving further gains while using Agile Data Engine (ADE).
For those who don't know, Agile Data Engine is a DataOps powerhouse for creating and maintaining Data products. It handles data modelling and all the complexities of things like Data Vault and Medallion architectures in a single, metadata-driven platform. Users typically report 4-5x productivity gains just by adopting ADE, because they stop writing manual SQL or Python and start focusing on intent and data modeling. The engine handles all the execution, such as the deployments and workflow orchestration. Testimonies like ‘development cycles went from weeks to days’ or ‘our data platform maintenance used to be 80% of budget, now it is 20%’ are common.
But if the engine is already doing the heavy lifting, where does AI fit in? Can you still expect major gains?
The engineers I interviewed are using agentic workflows to describe their intent and iterate the desired data model. They prefer to work with tools of their choice such as Cortex Code or GitHub Copilot etc. These tools can communicate with Agile Data Engine through its APIs and MCP servers. Engineers deploy skills and prompt their intent and needed changes in their development environment. The AI then designs and implements the required data structures at lightning speed.
My Questions: So how is this different to generating code? Isn’t it the same thing with all the downsides for possible impacts of hallucinations or lack of governance? The answer is a big NO, this is very different.
Here is the magic: The AI doesn't just "write code"; it generates ADE-compatible metadata.
AI-generated code still leaves you with code to maintain. AI-generated metadata leaves you with a much easier to maintain platform.
Interestingly, the engineers noted that AI isn't the answer to everything. For minor tweaks or usual changes to a single load, the purpose-built ADE User Experience is still faster than prompting an agent as things go ‘right’ the first time with no need for reviewing and double-checking.
However, when you are generating hundreds of entities and columns which is very common during cloud migrations or early-stage projects, the power of Agentic workflows is simply unbeatable.
The true beauty of combining AI with Agile Data Engine is that the AI only needs to handle the intent (the metadata). ADE handles the execution:
In an AI-driven world, everything is faster. Your deployments cannot wait for a manual window next week; they need to be instant and rock-solid. Because ADE deals with metadata, there is no need for tedious code analysis or debugging "black box" AI scripts. The lineage remains clear, and the flow is easy to follow.
The engine manages changes in the target data structures automatically. Processing data in deployments is optimized for maximum throughput and minimum cost, and they work every time.
So, can AI take you from ‘Great to Exceptional’? It certainly appears to be the case with Agile Data Engine. But in this case AI should not be looked at just as an "add-on” that makes your team “faster”. When combined with a high-powered engine, it can really transform how data products are built. It allows going from intent to production much faster, still keeping the governance and control. When done right, a brand-new operating model for DataOps is born.