Dotmatics Introduces Luma Agent, an AI Co-Scientist for Scientific Workflows

May 13, 2026
Dotmatics has launched Luma Agent, an AI co-scientist within its Luma Scientific Intelligence Platform that can plan and execute complex scientific tasks using structured, ontology-backed data.

Dotmatics announced in a press release the launch of Luma Agent, a new AI capability within its Luma Scientific Intelligence Platform. The system acts as an AI co-scientist that plans and executes complex scientific work using natural language instructions. Built on structured, ontology-backed scientific data, the platform ensures that all actions and outputs are traceable, verifiable, and reproducible.

Luma Agent allows scientists to request tasks in natural language, such as analyzing data, managing workflows, or configuring platform settings. It generates a step-by-step plan, executes it across multiple systems, and provides complete results. Each action is logged with full audit trails, and human approval is required before any data changes occur.

The AI system integrates with external language models through Model Context Protocol, enabling other AI tools to query or configure Luma directly. This design allows teams to build schemas, set up data flows, and modify workflows through conversational interaction rather than manual setup.

According to Dotmatics, Luma Agent is designed to meet governance and traceability standards required in regulated environments. Its foundation in structured scientific data enables it to serve as a reliable node within broader AI workflows used in life sciences research and production.

We hope you enjoyed this article

Consider subscribing to one of our newsletters like Enterprise AI Brief or Daily AI Brief.

Also, consider following us on social media:

Free newsletter

Enterprise AI Brief

Weekly report on AI business applications, enterprise software releases, automation tools, and industry implementations.

Whitepaper

Tensordyne Napier: What If One Rack Could Do the Work of Nine?

Tensordyne

This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.

Read more
Free, six days a week

Daily AI Brief: the AI news that matters, in your inbox.