On June 30, 2026, Anthropic introduced Claude Science, an AI "workbench" for scientists. It’s news that sounds distant—it talks about genomics, proteomics, or protein structure prediction—but behind it lies a trend that does directly affect private business: multi-agent AI applied to real work, with traceable and reproducible results. And there, even if you don’t have a laboratory, there are very useful lessons for your SME.
In this article we tell you what Claude Science is, what it represents for the market, and, above all, what you can start applying today in your business with good judgment, without chasing the latest headline.
What Claude Science is and why it’s news
According to Anthropic, Claude Science brings together in a single environment the scattered tools a researcher uses (databases, code notebooks, computing clusters…) and allows them to run multi-step analyses. Its great promise is that each result is documented: the figure or the report is generated alongside the code and the history that produced them, so that anyone can validate and reproduce them months later.
What’s interesting for us isn’t biology. It’s the pattern: an AI that executes a complete workflow, coordinates specialized "sub-agents," and submits its own work to review before considering it done.
What’s new about multi-agent AI
Until recently we asked AI for an answer and copied it. Multi-agent AI changes the approach: a coordinating agent distributes the task among several specialized agents, each solves its part, and a "reviewer" agent checks the whole.
In the case of Claude Science, Anthropic describes a reviewer agent that verifies citations and calculations, and flags or corrects errors on the fly. One of the cited researchers even set up "creator–critic" pairs: one agent writes and another evaluates accuracy and sources.
Translated into business language, this is what in our glossary we call an AI agent: a system that executes multi-step tasks using tools, not a chat that only answers questions. And the automatic review between agents is the mechanism to tackle the great risk of generative AI: hallucinations, that incorrect information with the appearance of being true.
Three lessons for your SME (even if you don’t have a laboratory)
1. Reproducibility isn’t just for scientists
If Claude Science saves "how each result was made," it’s because in science a figure without an origin is worthless. In an SME the same thing happens: a margin report, a purchasing forecast, or a settlement without traceability generates distrust and hours of manual review.
The lesson: any AI or data project should be born with traceability and governance from day one. Knowing which data each number comes from is what turns AI into a reliable tool for making decisions, and not into a generator of well-written guesses.
2. Agents execute, not just respond
The leap in value lies in going from "the AI suggests to me" to "the AI does and then someone validates." In the SME arena, this is called intelligent automation: combining process automation with AI for tasks that previously required human judgment, such as classifying invoices, preparing an order, or drafting a response.
You don’t need a scientific workbench to get started. With tools from the Microsoft ecosystem—Power Automate for the process and Power BI to see it—an SME can automate real administrative workflows and free up the team’s time.
3. Without organized data, AI doesn’t perform
Claude Science works because it connects to well-structured data sources. It’s the golden rule we always repeat: without organized and governed data, AI doesn’t deliver results. Before dreaming of agents, many companies first need to put their information in order: where it is, who accesses it, with what quality.
That’s why the sensible starting point is almost never "set up an AI," but rather choosing a specific and measurable use case and verifying that the data it needs exists and is reliable.
How we ground this at TISA
At TISA we have been helping private companies and SMEs digitalize since 1987, and we read this type of news with a very practical filter: how much of this is realistic today for a company like yours?
Our approach to AI applied to business begins with advice: identifying the relevant data, establishing data governance protocols, and evaluating which tools fit your investment capacity. From there, we develop a use case tailored to your business, instead of "doing AI for the sake of it."
All of this on a solid foundation: Microsoft Dynamics 365 Business Central as the ERP to have management data in order, and Power Platform (Power Apps, Power Automate, Power BI) to extend processes and automate. It’s the same principle that makes an environment like Claude Science useful—agents, automation, and governed data—but sized to the reality of an SME.
Let’s talk about your use case
Multi-agent AI marks where technology is heading, but the value for your company lies in taking a well-chosen first step: a measurable use case, with reliable and traceable data behind it.
If you want to explore where AI and automation fit in your business, at TISA we offer you a no-obligation assessment. Write to us at info@grupotisa.com, call us at (+34) 971 305 885, or visit grupotisa.com. We help you separate the noise from what can truly improve your day-to-day.