D&B's database of 642 million businesses was built for humans, not AI agents. So they rebuilt it.
AI agents were failing on business identity. D&B rebuilt its 642-million-company database from scratch — and the lessons apply to every enterprise.
Sean Michael Kerner
Context architecture is replacing RAG as agentic AI pushes enterprise retrieval to its limits
Enterprise retrieval is hitting its limits. Redis Iris makes the case that context architecture is the replacement — not better RAG.
Sean Michael Kerner
Enterprises can now train custom AI models from production workflows — no ML team required
Empromptu's Alchemy Models captures validated outputs from production AI apps and feeds them into a continuous fine-tuning loop — no data prep or ML expertise required.
Sean Michael Kerner
The RAG era is ending for agentic AI — a new compilation-stage knowledge layer is what comes next
The vector database category is undergoing a shift in response to the needs of agentic AI.
The retrieval rebuild: Why hybrid retrieval intent tripled as enterprise RAG programs hit the scale wall
Three months of VB Pulse data reveal an enterprise market in active retreat from the RAG infrastructure it spent 2025 building.
Sean Michael Kerner
Definity embeds agents inside Spark pipelines to catch failures before they reach agentic AI systems
The Chicago startup installs a JVM agent directly inside the pipeline execution layer — intervening during a run, not after it.
Sean Michael Kerner
RAG precision tuning can quietly cut retrieval accuracy by 40%, putting agentic pipelines at risk
Fine-tuning your RAG embeddings for precision may be quietly breaking retrieval, and standard fixes like hybrid search and reranking won't catch it.
Sean Michael Kerner
The modern data stack was built for humans asking questions. Google just rebuilt its for agents taking action.
Google rebuilt its data stack around AI agents — automating metadata curation, enabling cross-cloud queries with no egress fees, and ditching pipeline-writing for intent-driven engineering.
Sean Michael Kerner
Databricks tested a stronger model against its multi-step agent on hybrid queries. The stronger model still lost by 21%.
When queries span databases and documents, better models don't fix the problem. Databricks research shows why architecture does.
Sean Michael Kerner
Intuit compressed months of tax code implementation into hours — and built a workflow any regulated-industry team can adapt
No pitch, no IRS forms, hard deadline: inside the AI tooling Intuit built to ship accurate tax code under pressure.
Sean Michael Kerner
Amazon S3 Files gives AI agents a native file system workspace, ending the object-file split that breaks multi-agent pipelines
Gartner and IDC analysts say S3 Files removes the last friction point between enterprise data lakes and autonomous AI.
Sean Michael Kerner
Oracle converges the AI data stack to give enterprise agents a single version of truth
Matt Kimball, vice president and principal analyst at Moor Insights and Strategy, told VentureBeat the data layer is where production constraints surface first.
Sean Michael Kerner