UiPath announced new capabilities that enable the orchestration of Microsoft Copilot Studio agents alongside UiPath and other third-party agents using UiPath Maestro™, an enterprise orchestration solution to seamlessly coordinate agents, robots, and people across complex processes. Developers can now orchestrate Microsoft Copilot Studio agents directly from Maestro. This capability builds on bi-directional integration between the UiPath Platform™ and Microsoft Copilot Studio recently announced by Microsoft, that facilitates seamless interaction between UiPath and Microsoft agents and automations — allowing customers to automate complex end-to-end processes, enable contextual decision-making, improve scalability, and unlock new levels of productivity. Developers can now embed UiPath automations and AI agents directly into Microsoft Copilot Studio and integrate Copilot agents within UiPath Studio— all while orchestrating seamlessly across platforms with UiPath Maestro. UiPath Maestro can leverage the bi-directional integration with Copilot Studio to give customers built-in capabilities to build, manage, and orchestrate agents built in Microsoft Copilot Studio and other platforms in a controlled and scalable way—all while driving tangible business outcomes. Johnson Controls enhanced an existing automation—originally built with UiPath robots and Power Automate—by adding a UiPath agent for confidence-based document extraction. The result: a 500% return on investment and projected savings of 18,000 hours annually that were previously spent on manual document review. The integration extends other new capabilities that elevate business processes and drive smarter outcomes with agentic automation across departments and platforms.
LandingAI’s agentic vision tech uses an iterative workflow to accurately extract a document’s text, diagrams, charts and form fields to produce an LLM-ready output
LandingAI, a pioneer in agentic vision technologies, announced the major upgrades to Agentic Document Extraction (ADE). Unlike traditional optical character recognition (OCR), ADE sees a PDF or other document visually, and uses an iterative workflow to accurately extract a document’s text, diagrams, charts, form fields, and so on to produce an LLM-ready output. ADE utilizes layout-aware parsing, visual grounding, and no-template setup, allowing for quick deployment and dependable outcomes without the need for fine-tuning or model training. A leading healthcare platform provider, Eolas Medical, is processing over 100,000 clinical guidelines in the form of PDFs and complex documents with ADE, streamlining the creation of structured summaries with the view to supporting over 1.2million queries per month from healthcare professionals on their platform. Their QA chatbot, powered by ADE, provides answers with direct references to the original documents, improving information traceability and reliability. In financial services, ADE is being used to automate document onboarding for use cases like Know Your Customer (KYC), mortgage and loan processing, and client due diligence. Visual grounding enables full auditability by linking extracted data directly to its source location in the document.
Nvidia has launched Parakeet-TDT-0.6B-v2, an automatic speech recognition (ASR) model that can transcribe 60 minutes of audio in 1 second with an average “Word Error Rate” of just 6.05%
Nvidia has launched Parakeet-TDT-0.6B-v2, an automatic speech recognition (ASR) model that can, “transcribe 60 minutes of audio in 1 second [mind blown emoji].” This version two is so powerful, it currently tops the Hugging Face Open ASR Leaderboard with an average “Word Error Rate” (times the model incorrectly transcribes a spoken word) of just 6.05% (out of 100). To put that in perspective, it nears proprietary transcription models such as OpenAI’s GPT-4o-transcribe (with a WER of 2.46% in English) and ElevenLabs Scribe (3.3%). The model boasts 600 million parameters and leverages a combination of the FastConformer encoder and TDT decoder architectures. It can transcribe an hour of audio in just one second, provided it’s running on Nvidia’s GPU-accelerated hardware. The performance benchmark is measured at an RTFx (Real-Time Factor) of 3386.02 with a batch size of 128, placing it at the top of current ASR benchmarks maintained by Hugging Face. Parakeet-TDT-0.6B-v2 is aimed at developers, researchers, and industry teams building applications such as transcription services, voice assistants, subtitle generators, and conversational AI platforms. The model supports punctuation, capitalization, and detailed word-level timestamping, offering a full transcription package for a wide range of speech-to-text needs. Developers can deploy the model using Nvidia’s NeMo toolkit. The setup process is compatible with Python and PyTorch, and the model can be used directly or fine-tuned for domain-specific tasks. The open-source license (CC-BY-4.0) also allows for commercial use, making it appealing to startups and enterprises alike. Parakeet-TDT-0.6B-v2 is optimized for Nvidia GPU environments, supporting hardware such as the A100, H100, T4, and V100 boards. While high-end GPUs maximize performance, the model can still be loaded on systems with as little as 2GB of RAM, allowing for broader deployment scenarios.
Zilliz Cloud is supporting powering database infrastructure of AI applications through sub-10ms latency, zero downtime and outages, 70% savings in infrastructure costs, 10% improvement in search accuracy and 8% faster responsesdelivers sub-10ms latency and cost savings for AI-first companies
Organizations implementing Zilliz Cloud are experiencing transformative performance improvements that directly impact their AI applications: 1) CX Genie doubled query performance after migrating to Zilliz Cloud, reducing latency to just 5–10ms across over 1 million embeddings. The team eliminated recurring downtime and improved global service reliability — critical for its always-on AI-powered customer support. Latency: 2× faster, now 5–10ms; Uptime: Zero daily downtime; Costs: 70% infrastructure savings. 2) Beatoven.ai, an AI-powered music creation platform, shortened generation time by 2–3 seconds per track after adopting Zilliz Cloud — improving creative workflows for its 1.5 million+ users. Tracks: Over 6 million AI-generated; Speed: 2–3s faster music creation; Costs: 6× reduction in operational spend. 3) Ivy.ai powers AI chatbots for higher education and government institutions. As data volumes surged by 200%, Zilliz Cloud enabled them to maintain consistent response times without a single outage. Data growth: +200%; Reliability: Zero outages; Consistency: Stable response times at scale 4) Dopple Labs uses Zilliz Cloud to store and retrieve long-term memory embeddings for Dopple.ai, its virtual AI companion. By improving context awareness across conversations, Dopple now offers more natural, personalized interactions. Context: Improved memory across sessions; Interactions: More personalized, human-like dialogue 5) EviMed, a medical AI platform, integrated Zilliz Cloud to manage 350M+ medical knowledge entries. They achieved better search accuracy and faster responses while cutting system costs. Accuracy: +10% in clinical search precision; Speed: +8% faster responses; Efficiency: 30% lower operational cost. The results reported by Zilliz Cloud customers show that database infrastructure is no longer just backend plumbing — it’s a core driver of AI performance, reliability, and cost-efficiency. The ability to deliver sub-10ms latency, reduce outages, and cut operational costs gives AI teams a powerful edge in a competitive market.
Nvidia’s new AI marketplace to offer developers a unified interface to tap into an expanded list of GPU cloud providers for AI workloads in addition to hyperscalers
Nvidia is launching an AI marketplace for developers to tap an expanded list of graphics processing unit (GPU) cloud providers in addition to hyperscalers. Called DGX Cloud Lepton, the service acts as a unified interface linking developers to a decentralized network of cloud providers that offer Nvidia’s GPUs for AI workloads. Typically, developers must rely on cloud hyperscalers like Amazon Web Services, Microsoft Azure or Google Cloud to access GPUs. However, with GPUs in high demand, Nvidia seeks to open the availability of GPUs from an expanded roster of cloud providers beyond hyperscalers. When one cloud provider has some idle GPUs in between jobs, these chips will be available in the marketplace for another developer to tap. The marketplace will include GPU cloud providers CoreWeave, Crusoe, Lambda, SoftBank and others. The move comes as Nvidia looks to address growing frustration among startups, enterprises and researchers over limited GPU availability. With AI model training requiring vast compute resources — especially for large language models and computer vision systems — developers often face long wait times or capacity shortages. Nvidia CEO Jensen Huang said that the computing power needed to train the next stage of AI has “grown tremendously.”
Snyk launches real-time governance and adaptive policy enforcement, crucial for managing evolving risks in AI-driven software development
Cybersecurity company Snyk launched the Snyk AI Trust Platform, an AI-native agentic platform designed to empower organizations to accelerate AI-driven innovation, mitigate business risk and secure agentic and generative AI. The platform introduces several innovations, including Snyk Assist, an AI-powered chat interface offering contextual guidance, next-step recommendations and security intelligence. Another feature called Snyk Agent further extends these capabilities by automating fixes and security actions throughout the development lifecycle, leveraging its testing engines. Other parts of the offering include Snyk Guard, which provides real-time governance and adaptive policy enforcement, crucial for managing evolving AI risks. Complementing these capabilities is the Snyk AI Readiness Framework, which helps organizations assess and mature their secure AI development strategies over time. Also launching from Snyk are two new platform-supporting curated AI Trust environments. Snyk Labs is an innovation hub for researching, experimenting with and incubating the future of AI security, while Snyk Studio allows technology partners to collaborate with Snyk experts to build secure AI-native applications for mutual customers. With Snyk Studio, developers and technology providers can collaborate with its security experts to embed critical security context and controls into their AI-generated code and AI-powered workflows.
IBM watsonx to support enterprise-grade AI solutions at the edge with Lumen’s edge network offering <5ms latency
Lumen and IBM announced a new collaboration to develop enterprise-grade AI solutions at the edge—integrating watsonx, IBM’s portfolio of AI products, with Lumen’s Edge Cloud infrastructure and network. The new AI inferencing solutions optimized for the edge will deploy IBM watsonx technology in Lumen’s edge data centers and leverage Lumen’s multi-cloud architecture, enabling clients across financial services, healthcare, manufacturing and retail to analyze massive volumes of data in near real-time to help minimize latency. This will allow enterprises to develop and deploy AI models closer to the point of data generation, facilitating smarter decision-making while maintaining data control and security, plus accelerating AI innovation. Lumen’s edge network offers <5ms latency and direct connectivity to major cloud providers and enterprise locations. When paired with IBM watsonx, the infrastructure has the potential to enable real-time AI processing, which can help mitigate costs and risks associated with public cloud dependence. IBM Consulting will act as the preferred systems integrator, supporting clients in their efforts to scale deployments, reduce their costs and fully leverage AI capabilities through their deep technology, domain, and industry expertise. The collaboration aims to solve contemporary business challenges by turning AI potential into practical, high-impact outcomes at the edge. For enterprise businesses, this can mean faster insights, lower operational costs, and a smarter path to digital innovation. Ryan Asdourian, Chief Marketing and Strategy officer at Lumen said, “By combining IBM’s AI innovation with Lumen’s powerful network edge, we’re making it easier for businesses to tap into real-time intelligence wherever their data lives, accelerate innovation, and deliver smarter, faster customer experiences.”
Anaconda supports enterprise open source, combining trusted distribution, simplified workflows, real-time insights, and governance controls in one place to deliver secure and production-ready enterprise Python
Anaconda announced the release of the Anaconda AI Platform, the only unified AI platform for open source that provides proven security and governance when leveraging open source for AI development, empowering enterprises to build reliable, innovative AI systems without sacrificing speed, value, or flexibility. As the only AI platform for open source, the Anaconda AI Platform combines trusted distribution, simplified workflows, real-time insights, and governance controls in one place to deliver secure and production-ready enterprise Python. The Anaconda AI Platform empowers organizations to leverage open source as a strategic business asset, providing the essential guardrails that enable responsible innovation while delivering documented ROI and enterprise-grade governance capabilities. The Anaconda AI Platform enables enterprises to build once and deploy anywhere safely and at scale. Anaconda saw a 119% ROI and $1.18M in benefits within three years, with improved operational efficiency (80% improvement worth $840,000, according to the Forrester study) and enterprise-powered security (Anaconda provided an 80% reduction in time spent on package security management and a 60% reduction in security breach risk, according to the Forrester study). The Anaconda AI Platform eliminates environment-specific barriers, enabling teams to create, innovate, and run AI applications across on-premise, sovereign cloud, private cloud, and public cloud on any device without reworking code for each target. The platform is now available on AWS Marketplace for seamless procurement and deployment. Additional features include: Trusted Distribution; Secure Governance; Actionable Insights
Microsoft’s new tools can build and manage multi-agent workflows and simulate agent behavior locally before deploying to the cloud while ensuring interoperability across different open-source frameworks like MCP and Agent2Agent
Microsoft Corp. is rolling out a suite of new tools and services that are designed to accelerate the development and deployment of the autonomous assistants called artificial intelligence agents across its platforms. The Azure AI Foundry Agent Service is now generally available, allowing developers to build, manage, and scale AI agents that automate business processes. It supports multi-agent workflows, meaning specialized agents can collaborate on complex tasks. The service integrates with various Microsoft services and supports open protocols like Agent2Agent and Model Context Protocol, ensuring interoperability across different agent frameworks. To streamline deployment and testing, Microsoft has introduced a unified runtime that merges the Semantic Kernel SDK and AutoGen framework, enabling developers to simulate agent behavior locally before deploying to the cloud. The service also includes AgentOps, a set of monitoring and optimization tools, and allows developers to use Azure Cosmos DB for thread storage. Another major announcement is Copilot Tuning, a feature that lets businesses fine-tune Microsoft 365 Copilot using their own organizational data. This means law firms can create AI agents that generate legal documents in their house style, while consultancies can build Q&A agents based on their regulatory expertise. The feature will be available in June through the Copilot Tuning Program, but only for organizations with at least 5,000 Microsoft 365 Copilot licenses. Microsoft is also previewing new developer tools for Microsoft Teams, including secure peer-to-peer communication via the A2A protocol, agent memory for contextual user experiences, and improved development environments for JavaScript and C#.
Mistral AI’s ‘plug and play’ platform offers built-in connectors to run Python code, create custom visuals, access documents stored in cloud and retrieve information from web for easy customization of AI agents
French AI startup Mistral AI is introducing its Agents API, a “plug and play” platform that enables third-party software developers to quickly add autonomous generative AI capabilities to their existing applications. The API uses Mistral’s proprietary Medium 3 model as the “brains” of each agent, allowing for easy customization and integration of AI agents into enterprise and developer workflows. The API complements Mistral’s existing Chat Completion API and focuses on agentic orchestration, built-in connectors, persistent memory, and the ability to coordinate multiple AI agents to tackle complex tasks. This innovative approach aims to overcome the limitations of traditional language models. The Agents API comes equipped with several built-in connectors, including: Code Execution: Securely runs Python code, enabling applications in data visualization, scientific computing and other technical tasks. Image Generation: Leverages Black Forest Lab FLUX1.1 [pro] Ultra to create custom visuals for marketing, education or artistic uses. Document Library: Accesses documents stored in Mistral Cloud, enhancing retrieval-augmented generation (RAG) features. Web Search: Allows agents to retrieve up-to-date information from online sources, news outlets and other reputable platforms.