A CRM without a golden record is full of duplicates. An ERP mid-migration is throwing inconsistencies. A data warehouse with a failed consolidation job is quietly lying to everyone downstream.
Without the right logic, every automation built on top of that data fails.
Two years ago, this wasn’t a crisis — most of it was manual, or handled by batch jobs that ran at 2am and nobody watched.
But now there are agents. Autonomous, always-on, always-deciding. So when the data turns inconsistent: big problemz(!).
How does an agent reason through a contradiction it has no framework to handle? It doesn’t. It breaks.
There Is a Way Out
This week I read a formal logic paper that addresses exactly this problem — and does it in a way I find useful well outside academia.
The core idea is simple: evidence and truth are not the same thing.
Evidence can point in a direction without being definitive. That partial signal is still usable — even when contradictory evidence exists alongside it.
To prevent the system from collapsing under contradictory data, the researchers built a logic around what they call a “classicality stamp”: applied to a logical proposition, it marks that piece of information as reliable — safe to reason about with standard if-then-else logic.
Without that stamp, the system doesn’t crash. Instead, it manages the uncertainty, progressively reducing it until it can be resolved into a classical logical state.
The Algorithm That Always Terminates
The researchers also proved that there is always an algorithm that terminates with a “classical” answer: decidable, automatable.
Why does this matter to me, as someone running technology for marketing organisations?
Because we are building AI and automation systems on logical foundations that were never designed for the messy, fragmented, contradictory data that actually exists inside companies.
This work guarantees that people like me will be able to build automations on data that isn’t always clean — on sources that contradict each other — and still get a decision out the other end.
Everything eventually becomes decidable for an LLM. Now there’s formal logic to back that up.
The Strategic Move
If you are designing rule engines, knowledge graphs, or inference systems on enterprise data, read this Google DeepMind paper now: https://lnkd.in/dmvDzKqb
I’m not telling you to become a scholar of paraconsistent logic. I’m telling you that you can stop pretending your data is clean just to keep a batch job happy.
As soon as this theorem gets absorbed into the decision logic of LLMs, your tools will change — and they will handle most inconsistencies regardless of whether the underlying problem ever gets fixed.