Discussion on "Where AI Agents Belong in Data Engineering: The Correctness Layer"

Simon Späti · 2026-06-29T16:47:17.898Z

With ever-changing models, new and better ones coming out every few months, it's great if we don't have to rely on them too heavily. The better your tooling, the less dependent you become on any singl

I like the emphasis on correctness over simply relying on AI-generated outputs. It's a good reminder that reliable data engineering isn't just about using smarter AI—it's about combining AI with deterministic validation and solid engineering practices.

The deterministic layer idea makes sense. LLMs for intent, not for final correctness.

LLMs for brainstorming, ideas, but not for correctness, indeed.

Great article 👍🏼

One pattern I've started noticing is that correctness isn't really a property of the model it's a property of the execution boundary.

General-purpose agents are great at proposing solutions, but the moment they cross into domains with strict semantics (SQL, infrastructure, finance, security), every probabilistic decision should eventually terminate in something deterministic.

I think the future architecture won't be "better agents" but thinner agents sitting on top of increasingly powerful deterministic engines query planners, lineage analyzers, policy engines, type checkers, compilers, AST transforms, etc.

That's also why swapping models is becoming easier than swapping the correctness layer. The model becomes replaceable, while the deterministic foundation becomes the real moat. That's a much more durable architecture for production AI.

every probabilistic decision should eventually terminate in something deterministic.

100%. Yes thinker layers, or what the article suggests, that you are not dependend on model must be the goal, so you can exchange the models, as the model uses a deterministic tool like a linter or compiler.

Search Hashnode

Where AI Agents Belong in Data Engineering: The Correctness Layer

Responses(9)