Fabi.ai announced the launch of Workflows, a revolutionary data insights pipeline feature that enables data and product teams to build automated, intelligent workflows that deliver personalized insights directly to stakeholders’ preferred tools. Unlike legacy BI platforms that create “dashboard graveyards,” Workflows meets business users where they actually work—in Slack, email, and Google Sheets—while leveraging AI in the data analysis process to generate meaningful summaries and actionable recommendations. The product addresses three critical failures of legacy BI: restricted data access that ignores real business workflows, misaligned incentives that prioritize seat sales over insight sharing, and the creation of static dashboards that users ultimately abandon for spreadsheets. Workflows transforms this paradigm by automating the delivery of fresh, AI-enhanced insights directly to the tools teams use daily, without forcing data experts to an advanced degree in the vendor’s tooling. Key capabilities of Workflows include: Universal Data Connectivity: Connect to any data source including Snowflake, Databricks, MotherDuck, Google Sheets, Airtable, and more; Integrated Processing Tools: SQL for querying, Python for advanced analysis, and AI for natural language processing and insight generation working seamlessly together; Smart Distribution: Automatically push AI-generated, customized insights via email, Slack, or Google Sheets on configurable schedules; AI-Powered Analysis: Leverage AI to process unstructured data, extract insights from notes and comments, and generate executive summaries; Python-Native Architecture: Enterprise-grade security with scalable AI processing capabilities
Meta’s new Llama API to use Cerebras ultra-fast inference tech that would allow developers build apps that require chaining multiple LLM calls while offering generation speeds up to 18X faster than traditional GPU-based solutions
Meta announced a partnership with Cerebras Systems to power its new Llama API, offering developers access to inference speeds up to 18 times faster than traditional GPU-based solutions. What sets Meta’s offering apart is the dramatic speed increase provided by Cerebras’ specialized AI chips. The Cerebras system delivers over 2,600 tokens per second for Llama 4 Scout, compared to approximately 130 tokens per second for ChatGPT and around 25 tokens per second for DeepSeek, according to benchmarks from Artificial Analysis. This speed advantage enables entirely new categories of applications that were previously impractical, including real-time agents, conversational low-latency voice systems, interactive code generation, and instant multi-step reasoning — all of which require chaining multiple large language model calls that can now be completed in seconds rather than minutes. The Llama API represents a significant shift in Meta’s AI strategy, transitioning from primarily being a model provider to becoming a full-service AI infrastructure company. By offering an API service, Meta is creating a revenue stream from its AI investments while maintaining its commitment to open models. The API will offer tools for fine-tuning and evaluation, starting with Llama 3.3 8B model, allowing developers to generate data, train on it, and test the quality of their custom models. Meta emphasizes that it won’t use customer data to train its own models, and models built using the Llama API can be transferred to other hosts—a clear differentiation from some competitors’ more closed approaches. Cerebras will power Meta’s new service through its network of data centers located throughout North America, including facilities in Dallas, Oklahoma, Minnesota, Montreal, and California. By combining the popularity of its open-source models with dramatically faster inference capabilities, Meta is positioning itself as a formidable competitor in the commercial AI space. For Cerebras, this partnership represents a major milestone and validation of its specialized AI hardware approach.
Anthropic’s new feature update to enable Claude to incorporate data from SaaS applications into its prompt responses while its Research tool to allow preparing detailed reports about user-specified topics with more thorough analysis
Anthropic updated Claude with a feature called Integrations that will enable the chatbot to access data from third-party cloud services. The company rolled out the capability alongside an enhanced version of Research, a tool it introduced last month. The latter feature enables Claude to prepare detailed reports about user-specified topics. Research can now perform the task more thoroughly than before. The new Integrations capability will enable Claude to incorporate data from software-as-a-service applications into its prompt responses. If customers wish to connect Claude to an application for which a prepackaged integration isn’t available, they can build their own. Anthropic estimates that the process takes as little as 30 minutes. According to the company, developers can further speed up the workflow by using a set of tools that Cloudflare introduced in March to ease such projects. Claude’s new connectors are powered by MCP, a data transfer technology that Anthropic open-sourced. It provides software building blocks that reduce the amount of work involved in connecting a LLM to external applications. OpenAI, Anthropic’s top competitor, rolled out MCP support to its Agents SDK last month. Anthropic added MCP to Claude immediately after open-sourcing the technology last year. Until now, however, the chatbot only supported connections to applications installed on the user’s computer, which limited the feature’s usefulness.
Salesforce’s new benchmark for tackling ‘jagged intelligence’ in CRM scenarios shows leading agents succeed less than 65% of the time at function-calling for the use cases of three key personas: service agents, analysts, and managers
To tackle “jagged intelligence” one of AI’s most persistent challenges for business applications: the gap between an AI system’s raw intelligence and its ability to consistently perform in unpredictable enterprise environments —Salesforce revealed several new benchmarks, models, and frameworks designed to make future AI agents more intelligent, trusted, and versatile for enterprise use. The SIMPLE dataset, a public benchmark featuring 225 straightforward reasoning questions designed to measure how jagged an AI system’s capabilities really are. Perhaps the most significant innovation is CRMArena, a novel benchmarking framework designed to simulate realistic customer relationship management scenarios. It enables comprehensive testing of AI agents in professional contexts, addressing the gap between academic benchmarks and real-world business requirements. The framework evaluates agent performance across three key personas: service agents, analysts, and managers. Early testing revealed that even with guided prompting, leading agents succeed less than 65% of the time at function-calling for these personas’ use cases. Among the technical innovations announced, Salesforce highlighted SFR-Embedding, a new model for deeper contextual understanding that leads the Massive Text Embedding Benchmark (MTEB) across 56 datasets. A specialized version, SFR-Embedding-Code, was also introduced for developers, enabling high-quality code search and streamlining development. Salesforce also announced xLAM V2 (Large Action Model), a family of models specifically designed to predict actions rather than just generate text. These models start at just 1 billion parameters—a fraction of the size of many leading language models. To address enterprise concerns about AI safety and reliability, Salesforce introduced SFR-Guard, a family of models trained on both publicly available data and CRM-specialized internal data. These models strengthen the company’s Trust Layer, which provides guardrails for AI agent behavior. The company also launched ContextualJudgeBench, a novel benchmark for evaluating LLM-based judge models in context—testing over 2,000 challenging response pairs for accuracy, conciseness, faithfulness, and appropriate refusal to answer. Salesforce unveiled TACO, a multimodal action model family designed to tackle complex, multi-step problems through chains of thought-and-action (CoTA). This approach enables AI to interpret and respond to intricate queries involving multiple media types, with Salesforce claiming up to 20% improvement on the challenging MMVet benchmark.
Postman’s agent framework enables developers to build AI agents by discovering the right APIs and LLMs, evaluating them across providers and testing them, and keeping them running reliably
In this exclusive episode of DEMO, Keith Shaw discusses the platform Postman, the world’s leading API collaboration platform. Postman is designed for developers and enterprises to build intelligent AI agents, simplifying the agent-building process, reducing platform sprawl, and unlocking the full potential of APIs and large language models. One key benefit of Postman is its suite to discover the right APIs and LLMs to use in agents, allowing users to test functionality, integrate, and build through the Flows experience all in one platform. Postman leverages internal APIs and connects to hundreds of thousands of public APIs, enabling agents to access tools like Slack, Notion, UPS, and more. The agent framework involves building agents, discovering APIs and models, evaluating and testing them, and keeping them running reliably. Postman’s core workspace includes a made-up company called ShelfWise, which stores all APIs used by the company. Postman supports multiple protocols like HTTP, GraphQL, and gRPC, and has introduced a new request type: LLMs. With the rise of AI, Postman offers options like OpenAI, Google, and Anthropic. Postman also allows users to evaluate multiple models across providers using a collection runner, which can be run manually or integrated into their CI/CD pipeline. It also provides visualization tools to help teams make smarter decisions. Postman AI Agent Builder is available on postman.com, where users can find collections, examples, and Flows to fork and use right away.
Apple and Anthropic are building AI-powered coding platform that generates code through a chat interface, tests user interfaces and manages the process of finding and fixing bugs
Apple and Anthropic have reportedly partnered to create a platform that will use AI to write, edit and test code for programmers. Apple has started rolling out the coding software to its own engineers. The company hasn’t decided whether to make it available to third-party app developers. The tool generates code or alterations in response to requests made by programmers through a chat interface. It also tests user interfaces and manages the process of finding and fixing bugs. Amazon, Meta, Google and several startups have also built AI assistants for writing and editing code. McKinsey said in 2023 that AI could boost the productivity of software engineering by 20% to 45%. This increased efficiency has far-reaching implications for businesses across industries, CPO and CTO Bob Rogers of Oii.ai told. AI-powered tools enable developers to create software and applications faster and with fewer resources. “Simple tasks such as building landing pages, basic website design, report generation, etc., can all be done with AI, freeing up time for programmers to focus on less tedious, more complex tasks,” Rogers said. “It’s important to remember that while generative AI can augment skills and help folks learn to code, it cannot yet directly replace programmers — someone still needs to design the system.”
StarTree integrates Model Context Protocol (MCP) support to its data platform to allow AI agents to dynamically analyze live, structured enterprise data and make micro-decisions in real time
StarTree announced two new powerful AI-native innovations to its real-time data platform for enterprise workloads: Model Context Protocol (MCP) support: MCP is a standardized way for AI applications to connect with and interact with external data sources and tools. It allows Large Language Models (LLMs) to access real-time insights in StarTree in order to take actions beyond their built-in knowledge. Vector Auto Embedding: Simplifies and accelerates the vector embedding generation and ingestion for real-time RAG use cases based on Amazon Bedrock. These capabilities enable StarTree to power agent-facing applications, real-time Retrieval-Augmented Generation (RAG), and conversational querying at the speed, freshness, and scale enterprise AI systems demand. The StarTree platform now supports: 1) Agent-Facing Applications: By supporting the emerging Model Context Protocol (MCP), StarTree allows AI agents to dynamically analyze live, structured enterprise data. With StarTree’s high-concurrency architecture, enterprises can support millions of autonomous agents making micro-decisions in real time—whether optimizing delivery routes, adjusting pricing, or preventing service disruptions. 2) Conversational Querying: MCP simplifies and standardizes the integration between LLMs and databases, making natural language to SQL (NL2SQL) far easier and less brittle to deploy. Enterprises can now empower users to ask questions via voice or text and receive instant answers, with each question building on the last. This kind of seamless, conversational flow requires not just language understanding, but a data platform that can deliver real-time responses with context. 3) Real-Time RAG: StarTree’s new vector auto embedding enables pluggable vector embedding models to streamline the continuous flow of data from source to embedding creation to ingestion. This simplifies the deployment of Retrieval-Augmented Generation pipelines, making it easier to build and scale AI-driven use cases like financial market monitoring and system observability—without complex, stitched-together workflows.
Speculation has resurfaced around a possible integration of Ripple’s XRP with SWIFT following integration by SBI Remit
A recent report by Mastercard, titled “Blockchain Technology Fuels New Remittances Business Cases,” highlights several examples of blockchain applications in remittance systems. Speculation has resurfaced around a possible integration of Ripple’s XRP with SWIFT, the global messaging network for cross-border transactions. Previous reports have indicated that banks have tested XRP’s compatibility with SWIFT. If confirmed, such a partnership could significantly boost XRP adoption among global financial institutions. The report also mentions SBI Remit, a Japanese money transfer service that uses XRP as a bridge currency. It places SBI alongside earlier examples such as MoneyGram and Stellar, suggesting a broader trend of using cryptocurrencies to cut costs and speed up cross-border transactions. Mastercard’s reference to Ripple and XRP adds credibility to the token’s role in remittances. It signals that mainstream payment firms are now taking a closer look at blockchain infrastructure. The inclusion gives Ripple added visibility in the financial ecosystem. SBI Remit’s ongoing use of XRP in Asia further illustrates how digital assets are being integrated into real-world payment systems. The Mastercard report underscores that blockchain solutions are being evaluated across regions and technologies.
AWS announces Q Developer agentic AI that will generate code using the entire codebase in the GitHub repository
Amazon Web Services (AWS) has introduced a preview for its agentic artificial intelligence software development assistant, Q Developer, for Microsoft Corp.’s open-source code repository GitHub. GitHub is a platform used by millions of developers to store vast amounts of source code for software projects, enabling collaboration, version control, and code management. Q Developer is now available in the GitHub Marketplace, providing AI-powered capabilities such as feature development, code review, and Java code migration directly within the GitHub interface. Q Developer acts as a teammate, automating tedious tasks. Developers can assign issues to it, such as feature requests, and it will generate code using the entire codebase in the GitHub repository by following the description in the request. The AI agent will automatically update the code repository with the changes, performing syntactically sound checks and using GitHub Actions for security vulnerability scans and code quality checks. It will also use its own feedback to improve the code. Q Developer also offers easy migration for legacy codebases, allowing developers to assign a GitHub issue called “Migration” and assign it to the Amazon Q transform agent. This agent will handle all of the migration from the earlier version of Java to the newest, ensuring developers have access to the most recent features and capabilities.
IBM’s hybrid technologies enable businesses to build and deploy AI agents with their own enterprise data- offering Agent Catalog in watsonx Orchestrate to simplify access to 150+ agents
IBM is unveiling new hybrid technologies that break down the longstanding barriers to scaling enterprise AI – enabling businesses to build and deploy AI agents with their own enterprise data. IBM is providing a comprehensive suite of enterprise-ready agent capabilities in watsonx Orchestrate to help businesses put them into action. The portfolio includes: 1) Build-your-own-agent in under five minutes, with tooling that makes it easier to integrate, customize and deploy agents built on any framework – from no-code to pro-code tools for any kind of user. 2) Pre-built domain agents specialized in areas like HR, sales and procurement – with utility agents for simpler actions like web research and calculations. 3) Integration with 80+ leading enterprise applications from providers like Adobe, AWS, Microsoft, Oracle, Salesforce Agentforce, SAP, ServiceNow, and Workday. 4) Agent orchestration to handle the multi-agent, multi-tool coordination needed to tackle complex projects like planning workflows and routing tasks to the right AI tools across vendors. 5) Agent observability for performance monitoring, guardrails, model optimization, and governance across the entire agent lifecycle. IBM is also introducing the new Agent Catalog in watsonx Orchestrate to simplify access to 150+ agents and pre-built tools from both IBM and its wide ecosystem of partners. IBM is also introducing webMethods Hybrid Integration5, a next-generation solution that replaces rigid workflows with intelligent and agent-driven automation. It will help users manage the sprawl of integrations across apps, APIs, B2B partners, events, gateways, and file transfers in hybrid cloud environments.