Agentic AI is moving into production loops, BI orchestration and CPU-GPU coordination. The main bottleneck is silent tool failures and execution harness reliability, not raw generation quality.
Researchers presented JAZ, an LLM agent framework that reduces the harness to a single primitive, invoke, capable of executing arbitrary code and exposing inputs and interaction history to the LLM as variables. Without manual tools or external system...
A study of 15 scientific tools integrated in ToolUniverse identified 91 silent failures in agent-tool interaction: failures where the API or wrapper returns incomplete responses without any notification. Most occur in the API and wrapper layers. Rese...
Intel shifts focus to agentic AI: CPU-GPU coordination becomes the centerpiece. The move signals a rethinking of hardware for workloads that do not end with a single inference but chain reasoning, tool calling, and memory. Direct implications for tho...
Shopify describes a continual learning loop built on PyTorch and vLLM: production failures become model weight updates. The GraphQL agent surpasses frontier-model quality and cuts serving costs by 96%, from an estimated $27 million to about $1 millio...
End-to-end BI exposes a structural limit of LLMs: below 50 percent accuracy without human guidance. The real gains come from architectures that orchestrate tools and post-training on proprietary data. For on-premise and self-hosted deployments, TCO s...
The BI-Bench benchmark, built from real projects and public dashboards, shows that even frontier LLMs remain below 50% accuracy in end-to-end business intelligence. BI-Agent, a tool-augmented agent that breaks down search, join, and transformation an...
