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One of the hardest unsolved problems in enterprise agentic AI is that all the data these agents need to read was written for humans not machines. "john's se
by Bhavagna 3mo ago
One of the hardest unsolved problems in
enterprise agentic AI is that all the data
these agents need to read was written for
humans not machines.
"john's service is down again, same as
last tuesday" makes perfect sense to anyone
on that team. An AI agent has to guess at
who john is, which service he owns, what
last tuesday refers to, and whether this
is a recurring pattern.
I've been working on this problem — built
an open source semantic normalization layer
that resolves entities, maps relationships,
and normalizes temporal references before
data reaches AI agents.
Tested on real kubernetes/kubernetes GitHub
issues: 99% entity resolution, 15/15 root
causes extracted, 64% token reduction vs
passing raw text directly.
Happy to share more if anyone is working
on similar problems.