OpenAI has developed an internal AI data agent that lets employees run complex data analyses using natural language. A key technique called "Codex Enrichment" crawls the codebase to understand what tables actually contain.<br /> The article OpenAI develops six-layer context system to help employees navigate 600 petabytes of data appeared first on The Decoder. [...]
Across 101 enterprises, the context feeding AI agents is failing often and repeatedly. Sixty-eight percent have traced a confident but wrong agent answer to missing or inconsistent business context in [...]
An enterprise AI agent answers with total confidence, but the number is wrong. Nobody catches it until someone traces it back to a stale metric definition or a document the retrieval system never pull [...]
Across 101 enterprises, the infrastructure that feeds AI agents their business context is being built faster than it can be trusted. Retrieval-augmented generation is already the default context sourc [...]
A company builds a governed context layer specifically to stop its AI agents from confidently giving wrong answers. Once that layer is live, the company is more than twice as likely to report the fail [...]
Enterprise AI agents have a new production failure mode, and it is not the model. As enterprises move from single-layer RAG to hybrid retrieval architectures, the same underlying data produces differe [...]
When an OpenAI finance analyst needed to compare revenue across geographies and customer cohorts last year, it took hours of work — hunting through 70,000 datasets, writing SQL queries, verifying ta [...]
When an OpenAI finance analyst needed to compare revenue across geographies and customer cohorts last year, it took hours of work — hunting through 70,000 datasets, writing SQL queries, verifying ta [...]
Enterprise AI has largely been built around context engineering. Teams connect enterprise systems, generate chunks and embeddings, build retrieval pipelines, and assemble the context needed by individ [...]
When Miro’s data team pointed AI agents directly at its Snowflake environment, the agents got the wrong answer more than 65% of the time. The problem wasn’t the model — it was context. With more [...]