Data Intelligence Agents (DIA): Autonomous coding agents for enterprise data integration and SQL generation
Researchers present Data Intelligence Agents (DIA), a production-deployed system of three autonomous coding agents (Data Interpreter, Schema Creator, Query Generator) that automate enterprise data integration workflows. Rather than generating text, the agents produce, execute, validate, and repair concrete artifacts (code, schemas, SQL) with shared memory for experience reuse. The Query Generator is evaluated across seven SQL benchmarks spanning four dialects and task categories, matching or surpassing best published results on all seven. The system is deployed in production for enterprise customers, making it a notable applied research contribution.
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Datasette Agent
Simon Willison describes a Datasette Agent, an AI agent built on top of the Datasette data exploration tool. The post appears to demonstrate an agent capable of querying and reasoning over SQLite databases via natural language. This represents a practical deployment of LLM-powered tooling for data analysis workflows.
Databricks AI Dev Kit: Toolkit for Coding Agents
Databricks Field Engineering has published an open-source Python toolkit on GitHub aimed at coding agents, accumulating 1,555 stars. The repository appears to provide utilities and scaffolding for building AI-powered development agents on the Databricks platform. It is a community/field engineering project rather than an official product release.
DABStep: Data Agent Benchmark for Multi-step Reasoning
Hugging Face introduces DABStep, a benchmark designed to evaluate data agents on multi-step reasoning tasks. The benchmark targets agentic systems that must perform complex, sequential data operations rather than single-step queries. It aims to fill a gap in evaluation tooling for realistic data analysis workflows involving tool use and chained reasoning.
Microsoft RD-Agent: automated AI-driven R&D for data and model development
Microsoft has released RD-Agent, an open-source Python framework aimed at automating high-value R&D processes in AI, with a focus on data and model development. The project positions AI as the driver of data-driven AI workflows, targeting industrial productivity use cases. With 13,500 GitHub stars, it has attracted meaningful community interest, and a technical report is available.
Inside OpenAI's In-House Data Agent
OpenAI describes the architecture and capabilities of an internal AI data agent built on GPT-5 and Codex, designed to reason over large datasets and return reliable analytical insights within minutes. The system incorporates memory components to handle complex, multi-step data queries at scale. This represents a concrete internal deployment of frontier models in an agentic, tool-using workflow. The post offers a rare look at how OpenAI itself operationalizes its own models for enterprise-style data analysis.
Data2Story: Multi-agent framework for end-to-end data journalism with verifiable claims
Researchers introduce Data2Story (Data Journalist Agent), a multi-agent framework that orchestrates specialized roles to transform raw data into multimodal news articles. A key innovation is an Inspector module that grounds every claim back to data, code, or external references, enabling verifiability. The system also generates interactive multimodal outputs (maps, audio) rather than static text and charts. Evaluation across 18 articles with 53 human participants shows competitive quality versus expert-written pieces, with particular strength in transparency and auditability, though human journalists retain an edge in editorial angle and creative design.
Giving Agents Computers — Ivan Burazin, Daytona
Latent Space interviews Daytona CEO Ivan Burazin about the company's infrastructure for giving AI agents secure compute environments. The discussion covers Daytona's bare metal sandbox architecture, 850K daily runs, 74% month-over-month growth, and their approach to RL-based evaluations for agent workloads. The piece positions Daytona as part of an emerging 'agent cloud' category providing isolated execution environments for autonomous AI systems.
K-Dense-AI/scientific-agent-skills: Ready-to-Use Agent Skills Library for Research and Engineering
A Python repository providing a collection of pre-built agent skills targeting research, science, engineering, analysis, finance, and writing tasks. The project has accumulated 24,087 stars with a notable single-day gain of 762 stars, indicating significant community traction. No detailed technical documentation is available from the snippet, but the scope suggests a modular agent tooling library.