Organizations are moving from single-shot LLM calls to agents that orchestrate tools, interact with production systems, and handle end-to-end tasks such as business intelligence and code changes. Real-world evaluations show both the promise and the silent failure modes of these agentic pipelines.
The belief that AI requires rewriting legacy systems is often wrong. Integration patterns such as middleware, retrieval-augmented generation, event-driven architectures, and the Strangler Fig treat AI as a new layer rather than a new foundation. The ...
A research group stress-tested forecasting agents to understand when retrieval, reasoning, market priors, or historical analogs deserve trust. Mechanism choice depends on the source. ReliabilityRoute steers behavior using reliability features, and a ...
A study on thousands of tutoring-session transcripts shows that using disagreement among multiple LLMs to target expert attention shortens codebook revision from months to days. Among three methods, Rationale Labeling reaches 64.9% accuracy against 5...
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...
