AI Orchestration

Opening — Why this matters now The industry has quietly reached an uncomfortable realization: throwing more tokens at a problem is no longer impressive—it’s expensive. Test-time scaling, once celebrated as a clever workaround to model limitations, is starting to look like an unhedged position. Generating 500–700× more tokens to approximate reasoning is not intelligence—it’s brute-force search with a rising cloud bill. ...

Opening — Why this matters now For the past two years, the AI narrative has been dominated by model size. Bigger models, better reasoning, broader capabilities. But there’s a quiet constraint emerging—one that has nothing to do with intelligence, and everything to do with execution. When AI meets real-world infrastructure—especially systems like exascale supercomputers—the bottleneck is no longer thinking. It’s orchestration. ...

Opening — Why this matters now As large language models quietly slide from novelty to infrastructure, a less glamorous question has become existential: who pays the inference bill? Agentic systems amplify the problem. A single task is no longer a prompt—it is a chain of reasoning steps, retries, tool calls, and evaluations. Multiply that by production scale, and cost becomes the bottleneck long before intelligence does. ...

Enterprise data workflows have long been a patchwork of scripts, schedulers, human-in-the-loop dashboards, and brittle integrations. Enter the “Data Agent”: an AI-native abstraction designed not just to automate, but to reason over, adapt to, and orchestrate complex Data+AI ecosystems. In their paper, “Data Agent: A Holistic Architecture for Orchestrating Data+AI Ecosystems”, Zhaoyan Sun et al. from Tsinghua University propose a new agentic blueprint for data orchestration—one that moves far beyond traditional ETL. ...