All news with #azure ai foundry tag

🛡️ Microsoft introduced MAI-Cyber-1-Flash inside its MDASH multi-model vulnerability harness, claiming a 95.95% CyberGym score when paired with GPT-5.4 and a 50% cost reduction versus its previous MDASH mix. The new model is limited to MDASH private preview through Azure AI Foundry and is not available as a standalone API. Microsoft says MAI-Cyber-1-Flash handles up to 90% of tasks while GPT-5.4 addresses the hardest 10%, but the headline score applies to the MDASH configuration rather than the model alone.

🔐 Microsoft announced Project Perception, an AI-driven service entering public preview on Aug. 3 that uses multiple AI agents to continuously evaluate and update enterprise security posture. The multi-model harness selects the best model for each task to balance quality and cost, and Microsoft also introduced MAI-Cyber-1-Flash, a specialist model trained to find vulnerabilities. Integrated agents perform red-, blue- and green-team playbooks to detect, triage, and remediate threats automatically.

🚀 Claude Opus 4.8 is now accessible in Microsoft Foundry, providing developers and enterprises with Anthropic’s most capable Opus model for coding, agentic tasks, and professional work. The update focuses on long-running workflows, deeper reasoning across documents and codebases, and more reliable tool use for multi-step automation. Teams can now evaluate and deploy the model within Foundry’s unified platform for building and operating AI applications.

🔎 The Forrester Total Economic Impact study modeled a composite $10B enterprise using Microsoft Foundry and estimated a 327% ROI over three years, with developer productivity identified as the largest contributor at $15.7M. Foundry reduced undifferentiated engineering work and improved technical team productivity up to 35%, with some teams seeing payback in as few as six months. Its unified platform, reusable knowledge bases, built-in evaluations, and agent controls also enabled organizations to decommission legacy tools and avoid infrastructure costs.

🚀Claude Sonnet 4.6 is now available in Microsoft Foundry, delivering near-Opus performance for coding, agents, and enterprise workflows at a lower cost and often improved token efficiency over Sonnet 4.5. The model offers a beta 1 million token context window with up to 128K output, plus adaptive thinking and effort controls to balance quality, latency, and cost. Sonnet 4.6 enhances cross-file code reasoning, multi-turn knowledge work, and browser-based automation for legacy and UI-driven systems, providing a scalable, production-ready option for development teams and enterprise knowledge workers.

🔒 Palo Alto Networks announced that Prisma AIRS now integrates natively with Azure AI Foundry, enabling direct prompt and response scanning through the Prisma AIRS AI Runtime Security API. The integration provides real-time, model-agnostic threat detection for prompt injection, sensitive data leakage, malicious code and URLs, and toxic outputs, and supports custom topic filters. By embedding security into AI development workflows, teams gain production-grade protections without slowing innovation; the feature is available now via an early access program.

🚀 Microsoft announced integration of Anthropic's Claude models into Microsoft Foundry, making Azure the only cloud to provide both Claude and GPT frontier models on a single platform. The release brings Claude Haiku 4.5, Sonnet 4.5, and Opus 4.1 to Foundry with enterprise governance, observability, and deployment controls. Foundry Agent Service, the Model Context Protocol, skills-based modularity, and a model router are highlighted as tools to operationalize agentic workflows for coding, research, cybersecurity, and business automation. Token-based pricing tiers for the Claude models are published for standard deployments.

🏥 Microsoft and UiPath describe how integrated agents from Azure AI Foundry and UiPath, orchestrated by UiPath Maestro, can operationalize AI within clinical workflows to surface and act on incidental radiology findings. The workflow uses UiPath medical record summarization agents to flag findings, Azure AI Foundry imaging agents to analyze PACS images and prior results, and UiPath agents to aggregate and forward consolidated follow-up reports to ordering clinicians. Microsoft says this agentic approach accelerates decision-making, reduces physician workload, and improves outcomes while maintaining compliance with DICOMweb and FHIR standards.

🚀 Microsoft and NVIDIA announced expanded AI infrastructure on Azure, bringing NVIDIA RTX PRO 6000 Blackwell Server Edition to Azure Local, new Nemotron and Cosmos models via Azure AI Foundry, and broader support for Run:ai and GB300 NVL72 supercomputing clusters. These updates enable on-premises and edge AI with cloud-like management, improved GPU utilization, and infrastructure tailored for frontier reasoning, multimodal workloads, and real-time inferencing. Microsoft also highlighted NVIDIA Dynamo optimizations for ND GB200-v6 VMs to boost inference throughput at scale.

🔁 Microsoft positions the signals loop — continuous capture of user interactions and telemetry with systematic fine‑tuning — as essential for building adaptive, reliable AI apps and agents. The post explains that simple RAG and prompting approaches often lack the accuracy and engagement needed for complex use cases, and that continuous learning drives sustained improvements. It highlights Dragon Copilot and GitHub Copilot as examples where telemetry‑driven fine‑tuning yielded substantial performance and experience gains, and presents Azure AI Foundry as a unified platform to operationalize these feedback loops at scale.

💡 Developers are accelerating AI adoption across industries by using copilots and agentic workflows to compress the software lifecycle from idea to operation. Microsoft positions tools like GitHub, Visual Studio, and Azure AI Foundry to connect models and agents to enterprise systems, enabling continuous modernization, migration, and telemetry-driven product loops. The shift moves developers from manual toil to intent-driven design, with agents handling upgrades, tests, and routine maintenance while humans retain judgment and product vision.

🚀 Azure AI Foundry now integrates new OpenAI models—GPT-image-1-mini, GPT-realtime-mini, and GPT-audio-mini—alongside safety upgrades to GPT-5. The rollout, with most customers able to get started on October 7, 2025, targets efficient, low-latency multimodal workloads for developers and enterprises. Microsoft also highlighted the open-source Microsoft Agent Framework, multi-agent workflows, unified observability, Voice Live API GA, and Responsible AI enhancements to accelerate production-grade agentic solutions.

🤖 The Microsoft Agent Framework is now in public preview inside Azure AI Foundry, offering an open-source SDK and runtime to simplify orchestration of multi-agent systems. Developers can prototype locally and deploy with built-in observability, durability, and compliance while integrating tools via OpenAPI, Agent2Agent (A2A), and the Model Context Protocol (MCP). Microsoft also previews stateful multi-agent workflows and has contributed multi-agent tracing standards to OpenTelemetry. Responsible AI controls and a generally available Voice Live API add governance and real-time voice capabilities for enterprise scenarios.

🔒 Microsoft and xAI have brought Grok 4 to Azure AI Foundry, combining a 128K-token context window, native tool use, and integrated web search with enterprise safety controls and compliance checks. The release highlights first-principles reasoning and enhanced problem solving across STEM and humanities tasks, plus variants optimized for reasoning, speed, and code. Azure AI Content Safety is enabled by default and Microsoft publishes a model card with safety and evaluation details. Pricing and deployment tiers are available through Azure.

🔧This wrap-up of the Agent Factory series lays out a repeatable blueprint for designing and deploying enterprise-grade AI agents and introduces the agentic web stack. It catalogs eight essential components—communication protocols, discovery, identity and trust, tool invocation, orchestration, telemetry, memory, and governance—and positions Azure AI Foundry as an implementation. The post stresses open standards such as MCP and A2A, emphasizes interoperability across organizations, and highlights observability and governance as core operational requirements.

🔒 Azure outlines a layered blueprint for building trustworthy, enterprise-grade AI agents. The post emphasizes identity, data protection, built-in controls, continuous evaluation, and monitoring to address risks like data leakage, prompt injection, and agent sprawl. Azure AI Foundry introduces Entra Agent ID, cross-prompt injection classifiers, risk and safety evaluations, and integrations with Microsoft Purview and Defender. Join Microsoft Secure on September 30 to learn about Foundry's newest capabilities.

🔗 Azure's Agent Factory blog emphasizes that interoperability is the key to moving agentic AI from isolated prototypes to enterprise-scale solutions. The post promotes open standards like Model Context Protocol (MCP) and Agent2Agent (A2A) to enable shared context, reusable tools, and cross-framework collaboration across runtimes such as Semantic Kernel. It explains how Azure AI Foundry combines these protocols with thousands of connectors, unified observability, and governance so agents can act across SaaS, legacy systems, and custom APIs without costly rewrites.

🤖 Ask Ralph is a conversational AI styling companion built on Azure OpenAI, available in the Ralph Lauren app in the US. It uses natural-language prompts to interpret open-ended requests, asks clarifying questions, and returns curated, fully stylized, visually presented and shoppable outfit recommendations drawn from real-time inventory. Powered by agentic AI capabilities, the experience plans, reasons, and acts to deliver personalized looks at scale. Microsoft positions this as part of broader Azure AI solutions for retail innovation.

🔧 Azure AI Foundry aims to accelerate the path from IDE prototypes to enterprise-grade AI agents. It emphasizes local-first prototyping, a single, consistent Model Inference API, and one-click deployment from VS Code and GitHub so developer code runs unchanged in production. Foundry supports popular frameworks like Semantic Kernel and AutoGen, embraces open protocols (MCP, A2A), and supplies prebuilt connectors, observability, and enterprise guardrails to scale agents securely.

🔒 Unit 42 describes a widespread flaw called Model Namespace Reuse that lets attackers reclaim abandoned Hugging Face Author/ModelName namespaces and distribute malicious model code. The technique can lead to remote code execution and was demonstrated against major platforms including Google Vertex AI and Azure AI Foundry, as well as thousands of open-source projects. Recommended mitigations include version pinning, cloning models to trusted storage, and scanning repositories for reusable references.