This week in AI updates: Google’s UCP standard, a redesigned Slackbot, and more (January 16, 2026)

Google unveils new open-source standard for agentic commerce

Google has announced a new open-source standard for agentic commerce called the Universal Commerce Protocol (UCP).

Developed in collaboration with a number of commerce companies, including Shopify, Etsy, Wayfair, Target, and Walmart, UCP establishes a common language and primitives for the commerce journey between consumer surfaces, businesses, and payment providers.

“As consumers embrace conversational experiences, they expect seamless transitions from brainstorming and research to final purchase. That means it’s critical to support real-time inventory checks, dynamic pricing, and instant transactions, all within the user’s current conversational context,” Google wrote in a blog post.

Newly redesigned Slackbot is now generally available

Salesforce announced that the newly redesigned Slackbot is now generally available, offering users an out-of-the-box AI agent that lives within Slack.

“By bringing the full power of the Agentic Enterprise where billions of workplace conversations already happen every week, working with enterprise-grade AI becomes as natural as talking to a coworker,” Salesforce wrote in an announcement.

According to Salesforce, Slackbot leverages context within Slack and connected tools to help find answers, organize work, create content, schedule meetings, and take action.

Kaggle introduces Community Benchmarks to allow for custom evaluations of AI models

Kaggle has announced that it now offers Community Benchmarks, enabling AI practitioners to design, run, and share their own benchmarks for evaluating AI models.

Kaggle is a community platform run by Google that offers models and resources for data scientists and machine learning practitioners. Last year, it had introduced Kaggle Benchmarks to provide evaluations from research groups, such as Meta’s MultiLoKo and Google’s FACTS suite benchmarks.

This latest announcement extends this to the community as a whole, allowing them to create benchmarks specific to their own use cases. According to Google, AI capabilities are evolving so quickly that the existing ways of benchmarking and evaluating them aren’t able to keep up. With Community Benchmarks, the company hopes to bridge this gap and provide a more flexible and transparent framework for evaluation.

Copilot Studio Extension now available in VS Code

Microsoft has announced the general availability of its Copilot Studio Extension for Visual Studio Code.

The extension allows developers to build and manage Copilot Studio agents directly from within their IDE.

According to Microsoft, the extension is useful because developers need to have similar controls and processes when developing agents as they do for other applications: source control, pull requests, change history, and repeatable deployments.

Box Extract intelligently pulls information from unstructured content to help with workflow automation

Box announced the launch of Box Extract, which intelligently pulls information from content and saves it as metadata, helping organizations automate workflows and accelerate decision-making by making information more easily accessible.

According to the company, a lot of organizational knowledge lives in contracts, product specifications, policy documents, charts, and other types of unstructured content. Box Extract utilizes agentic capabilities and AI models from Google, Anthropic, and OpenAI to accurately extract this information.

Box explained that legacy tools often focus only on extracting text, whereas Box Extract understands document structure and meaning. It breaks the document down into components like paragraphs, tables, and charts, and then pulls out important information from those components.

Google releases TranslateGemma

TranslateGemma is a suite of open translation models built on Gemma 3. They were trained and evaluated on 55 language pairs, and were additionally trained on almost 500 language pairs as a starting point for researchers even though they have not been evaluated yet.

According to Google, TranslateGemma significantly reduces error rates in translation compared to baseline Gemma models alone.

The 4B model is optimized for mobile and edge deployment, the 12B model is optimized for consumer laptops, and the 27B is designed for maximum fidelity and can run on something like a single H100 GPU or TPU in the cloud.

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