Llms are just too creative. They will explore the search space of probable paths to get to their answer. There's no way you can patch all paths

We had to build isolation at the infra level (literally clone the DB) to make it safe enough otherwise there was no way we wouldn't randomly see the DB get deleted at some point

Look into copy on write branching. We built this natively into our AI Data Engineer (https://tryardent.com) so it could make modifications to databases with 0 blast radius pretty much because yes it's impossible to make an LLM 100% safe if it has no proper guard rails preventing destructive actions

email me -> vikram@tryardent.com

We're building support for snowflake too if that's something you use

You can't trust any agent to be perfect with a real db so unless you find an infra level way to isolate it, you can't get rid of the problem

So we built a system that creates copy on write copies of your DB and allocates a copy for each agent run. This means a completely isolated copy of your DB with all your data that loads in under a second but zero blast radius risk to your actual system for the agent to operate on. When you're okay with the changes we have a "quick apply" to replay those changes onto your real db

Website is a little behind since we just launched our db sandboxing feature to existing customers and are making it public next week :)

If you want to try it email me -> vikram@tryardent.com

We’re Vikram and John, founders of ArdentAI (https://www.ardentai.io/).

We built ArdentAI to tackle the pain points of data engineering—time-consuming pipelines, manual transformations, and error-prone debugging. With ArdentAI, you can automate these tasks up to 100x faster.

What is it?

ArdentAI is an AI Agent that connects directly to your databases and handles all the heavy lifting. Think of it as ChatGPT, but for your data infrastructure—it builds, syncs, transforms, and fixes errors for you. It doesn't just write code, it performs actions.

Why ArdentAI?

Data engineers spend countless hours manually creating pipelines, integrating APIs, and transforming data into usable formats like star schemas. ArdentAI does this in minutes, directly on your existing stack.

Key Features:

• Autofix Airflow: Connect it to Airflow, and it will review failed job runs, and edit code to fix the pipeline.

• Natural Language Interface: Build and debug pipelines with plain English commands.

• Direct Integration: Works seamlessly with your existing data stack.

• Safe Mode: Approve changes before they’re executed.

• End-to-End Encryption: Keeps your data secure, always.

Try it for Free:

Start with our free Basic plan—no credit card required. Enjoy 10 job calls per month and core features, with options to scale as your team grows. Use this link -> (https://www.Ardentai.io/signup)

We’d love your feedback!

We’re looking for insights from the HN community, especially data engineers and teams tackling integration and pipeline challenges. Your thoughts will help shape ArdentAI as we work toward our vision of transforming how data teams operate. Thanks for reading—we’re here to answer your questions!

—Vikram and John

Comments URL: https://news.ycombinator.com/item?id=42481071

Points: 9

# Comments: 3

While not dealing with that kind of scale yet, our application (An AI Data Engineer that has done migration work for users) needs to do before and after comparisons and find diffs. We use a branchable DB to compute those changes efficiently (DoltGres)

Could be an interesting thing to consider since it's worked well for us for that part.

Our build if u wanna check that out too -> https://Ardentai.io

It drops directly into your stack, no new configuration needed

It has its own compute engine and will soon support spark to be able to dynamically perform large scale ETLs and data manipulation.

I also am working towards supporting automatic data pipeline building and data quality checks.

It's live right now @ https://Ardentai.io

Check it out :)