AI Insight
Carnot is a new interactive execution engine designed to make AI-driven data analysis more transparent and controllable for enterprise users. Unlike existing "black box" AI research agents, Carnot converts natural language queries into visible execution graphs that users can inspect, modify, and execute step-by-step through a notebook interface. The system includes a query optimizer that can balance API costs and execution speed according to user-specified constraints, while allowing analysts to verify intermediate results and correct errors before they propagate.
Why it matters
This work addresses critical enterprise needs for trustworthy AI-driven analytics by giving analysts the ability to understand and control how AI systems retrieve and process their data. The interactive approach could reduce costs, prevent hallucination-based errors, and increase confidence in AI-generated insights for business-critical decisions.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Abstract: Enterprises increasingly seek to query data lakes using natural language via AI-driven tools like semantic operators or deep research agents. However, the latter operates as an opaque black box, hiding its intermediate reasoning and data retrieval steps, and failing to expose controls for managing API costs and execution latency. Meanwhile, the former can be prohibitively expensive for enterprise-scale data lakes. Consequently, analysts using these systems lack the agency to intercept hallucinated premises, verify intermediate results, or correct the system’s trajectory. We present Carnot, an interactive execution engine for AI-driven analytics. Carnot compiles natural language requests into physical execution graphs and surfaces them through an interactive notebook interface. Rather than waiting blindly for a final output, users can critique the plan, incrementally execute operators, inspect intermediate data, or directly edit the underlying code or semantic operator instructions. Carnot’s query optimizer will optimize the query with respect to cost or latency constraints provided by the user. Our demo will showcase how Carnot helps users achieve efficient and verifiable insights on workloads motivated by real enterprise use cases.
Source: Carnot: Interpretable, Interactive, and Optimized Execution of Deep Research Queries