Destination
OpenAI develops six-layer context system to help employees navigate 600 petabytes of data

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. [...]

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venturebeat
Agent context layers: Enterprises governing their AI data are catching twice as many bad answers as the ones who aren't

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 [...]

Match Score: 240.89

venturebeat
57% of enterprises have watched AI agents be confidently wrong. The fix is an agentic context layer, but who has one?

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 [...]

Match Score: 142.01

venturebeat
The AI context gap: Enterprise AI organizations have a trust problem, not a retrieval problem — and most are still building the fix

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 [...]

Match Score: 138.56

venturebeat
Enterprises with AI context layers report agent failures at more than twice the rate of those without one

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 [...]

Match Score: 110.04

venturebeat
AI agents keep giving confident wrong answers. The context layer is enterprise AI's next production problem.

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 [...]

Match Score: 105.39

venturebeat
OpenAI's AI data agent, built by two engineers, now serves 4,000 employees — and the company says anyone can replicate it

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 [...]

Match Score: 94.98

venturebeat
OpenAI's AI data agent, built by two engineers, now serves thousands of employees — and the company says anyone can replicate it

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 [...]

Match Score: 94.98

venturebeat
Enterprise AI agents are only as reliable as the messiest documents behind them

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 [...]

Match Score: 87.90

venturebeat
SQL query logs hold the context AI agents need to stop hallucinating joins

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 [...]

Match Score: 83.07