How much semantic modeling do your AI agents need?
A meeting room booking example shows when SQL is enough, when a property graph helps, and what would justify RDF or OWL.
Writing
Hands-on articles and shorter notes. New writing is published here first, then on LinkedIn and Medium. Everything is in the RSS feed.
Series
A series on ontology-grounded agents. Retrieval finds text that resembles a question. An enterprise needs the records that satisfy it, with evidence.
A meeting room booking example shows when SQL is enough, when a property graph helps, and what would justify RDF or OWL.
In a synthetic test, an AI agent returned 3 correct patients and quietly missed 9. Here is why that happened, and how to check whether a list of your own is complete.
A grocery substitution explains ontology, knowledge graphs, semantic and context layers, and how business and technology teams keep their definitions trustworthy.
The OAUTHBEARER SASL mechanism with custom callback handlers that validate tokens against Okta, wired to ACLs, tested end to end. Companion repo: kafka-oauth2.
Streams versus tables, keys and timestamps, filters, joins and aggregations, without writing a Kafka Streams topology. Docker based.
Consumer-lag monitoring with LinkedIn's Burrow, including the fork that added SCRAM-SHA-256 and SHA-512 support, a compose setup and a dashboard.
SCRAM-SHA-512 authentication, ACL authorization and SSL encryption, with the errors you hit along the way and how each one is fixed. Companion repo: SecureKafka.