Fabrix.ai Inc., previously known as CloudFabrix, delivers a purpose-built agentic AI operational intelligence platform that enables enterprise users to streamline IT operations use cases, make better decisions more quickly and successfully accelerate digital transformation. Fabrix.ai’s intelligent agents take over repetitive, time-consuming operational workloads for its enterprise customers, delivering increased agility and cost efficiency. There are three components to the Fabrix.ai operational platform: Agentic AI; Generative AI copilot; and Cisco-specific solutions. The company views its platform as having a unique capability to focus on automation, particularly in network observability. Running a network tends to be more stochastic than deterministic, so providing enterprises and service providers a solution requires additional building blocks, including guardrails, Model Context Protocol or agent-to-agent interfaces, and Fabrix.ai has built those. While Fabrix.ai continues to work closely with Cisco and telcos, the company is also branching out to serve customers in other areas, including AI security. One of the biggest differentiators for Fabrix.ai is the ability to work with real-time data. Fabrix.ai leverages many of the common building blocks, but the platform is purpose-built for IT ops use cases rather than trying to modify a generic AI model. Its focus on handling real-time information has enabled it to get traction in key verticals, especially telco. Fabrix.ai also leverages its growing partner ecosystem to bring its capabilities to more enterprise customers. The company can use whatever data platform a customer has, including Splunk, Elastic, OpenSearch, MinIO, HP or others. Or it could be a data lake, since it has partnerships with many of the data platforms and its data abstraction layer can read directly from the platforms.
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.
OpenAI’s new PDF export capability enables users to download comprehensive research reports with fully preserved formatting, tables, images, and clickable citations
OpenAI launched a new PDF export capability for its Deep Research feature, enabling users to download comprehensive research reports with fully preserved formatting, tables, images, and clickable citations. PDF export directly addresses a practical pain point for professional users who need to share polished, verifiable research with colleagues and clients. The technical implementation of PDF export transforms Deep Research from an interesting capability into a practical business tool by addressing several critical requirements for enterprise adoption. First, it bridges the gap between cutting-edge AI and traditional business communication. Second, the preservation of citations as clickable links addresses the critical need for verifiability in professional contexts. Without verifiable sources, AI-generated research lacks credibility in high-stakes decision-making environments. Most importantly, the PDF export capability dramatically improves Deep Research’s shareability. AI-generated insights create value only when they can be effectively distributed to decision-makers. By enabling users to generate professional-looking documents directly from research sessions, OpenAI removes a significant barrier to broader organizational adoption. The feature’s implementation across both new and past reports also demonstrates technical foresight. Rather than requiring users to adapt to AI-native interfaces for sharing research findings, the PDF export recognizes that many organizations still require traditional document formats for effective information distribution.
Salesforce to acquire Convergence.ai to accelerate the development of next-gen AI agents that can navigate dynamic interfaces and adapt in real time to manage web-based workflows and multi-step processes
Salesforce plans to acquire Convergence.ai to accelerate the development of its next-generation AI agents. The company signed a definitive agreement for the acquisition and expects Convergence’s team and technology to play a “central role” in advancing its AI agent platform, Agentforce. The acquisition is expected to close in the second quarter of Salesforce’s fiscal year 2026, subject to customary closing conditions. “The next wave of customer interaction and employee productivity will be driven by highly capable AI agents that can navigate the complexities of today’s digital work,” Adam Evans, executive vice president and general manager, Salesforce AI Platform at Salesforce, said. “Convergence’s innovative approach to building adaptive, intelligent agents is incredibly impressive.” Convergence’s technology enables AI agents to navigate dynamic interfaces and adapt in real time so they can manage things like web-based workflows and multi-step processes. The company’s talent is also expected to contribute to deep research, task automation and industry-specific solutions that will advance Salesforce’s broader AI roadmap.
Mistral AI’s agent framework combines its Medium 3 language model with persistent memory, tool integration and orchestration capabilities that allow maintaining context across conversations
Mistral AI released a comprehensive agent development platform that enables enterprises to build autonomous AI systems capable of executing complex, multi-step business processes. Mistral’s approach combines its Medium 3 language model with persistent memory, tool integration and orchestration capabilities that allow AI systems to maintain context across conversations while executing tasks like code analysis, document processing and web research. The timing suggests coordinated market movement toward standardized agent development frameworks. All the major agent development platforms now support the Model Context Protocol, an open standard created by Anthropic that enables agents to connect with external applications and data sources. This convergence indicates that the industry recognizes agent interoperability as a key determinant of long-term platform viability. Mistral’s approach differs from competitors in its emphasis on enterprise deployment flexibility. The company offers hybrid and on-premises installation options using as few as four GPUs, addressing data sovereignty concerns that prevent many organizations from adopting cloud-based AI services.
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.
Ally Bank’s AI platform can pick the right external LLM depending on the use case or combine answers from several LLMs; it removes PII, tracks all transactions and rehydrates PII for context
In an era where differentiation in banking is increasingly difficult, Ally Bank has emerged as a leader in creating exceptional digital banking experiences. Sathish Muthukrishnan, chief information and technology officer at Ally Financial said, “The intent behind launching our technology strategy was to ensure that technology will continue to be relevant in an all-digital bank, but more importantly, to create differentiation and drive significant business outcomes. We categorized our strategy into six different pillars. The first is security. Our second pillar was driving tremendous experiences. The third pillar is how I know my experience is working. That’s when data analytics came in. Measure what consumers do, but more importantly, measure what they don’t do. Our operational pillar involved migrating to cloud, driving automation and consistency in how we develop and deploy code. And then we needed to preserve our culture and take care of our talent. These pillars laid the foundation for our transformation. We now have about 75% of our applications running on the cloud and about 95% of the enterprise data in the cloud. This allows us to learn from consumer behaviors, understand what they’re expecting and create experiences in real time so consumers think they are our only customer. We had our cloud strategy and data in the cloud warehouse. At the beginning of 2022, we redefined our network. As we were thinking about AI, we launched our chat assistant, Ally Assist. We created Ally AI because we knew technology was fast-evolving, but there were concerns about sending data to external LLMs. To address this, we built an AI platform that could connect to external LLMs but with added security — it removes PII, tracks all transactions and rehydrates PII for context. Our platform can connect to multiple LLMs — from GPT to FLAN to Bedrock. We can pick the right LLM depending on the use case or combine answers from several LLMs. Our content creation LLM is different from what we use for code generation or risk assessment. We have different models for different use cases. My advantage is that the product team, UI/UX team and technology team are all part of the same technology organization. We rolled out savings buckets — your deposit account with multiple savings buckets that you can name yourself. If you start questioning why roadblocks exist and how to solve them, your brand becomes more relevant to consumers. You become their next best experience, deepening relationships.”
UiPath automations and agents can now integrate directly into Microsoft Copilot Studio to automate complex end-to-end processes at scale
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.