Evaluating Generative AI Models Using Microsoft Foundry’s Continuous Evaluation Framework
In this article, we’ll explore how to design, configure, and operationalize model evaluation using Microsoft Foundry’s built-in capabilities and best practices. Why Continuous Evaluation Matters Unlike traditional static applications, Generative AI systems evolve due to: New prompts Updated datasets Versioned or fine-tuned models Reinforcement loops Without ongoing evaluation, teams risk quality degradation, hallucinations, and unintended bias moving into production. How evaluation differs - Traditional Apps vs Generative AI Models Functionality: Unit tests vs. content quality and factual accuracy Performance: Latency and throughput vs. relevance and token efficiency Safety: Vulnerability scanning vs. harmful or policy-violating outputs Reliability: CI/CD testing vs. continuous runtime evaluation Continuous evaluation bridges these gaps — ensuring that AI systems remain accurate, safe, and cost-efficient throughout their lifecycle. Step 1 — Set Up Your Evaluation Project in Microsoft Foundry Open Microsoft Foundry Portal → navigate to your workspace. Click “Evaluation” from the left navigation pane. Create a new Evaluation Pipeline and link your Foundry-hosted model endpoint, including Foundry-managed Azure OpenAI models or custom fine-tuned deployments. Choose or upload your test dataset — e.g., sample prompts and expected outputs (ground truth). Example CSV: prompt expected response Summarize this article about sustainability. A concise, factual summary without personal opinions. Generate a polite support response for a delayed shipment. Apologetic, empathetic tone acknowledging the delay. Step 2 — Define Evaluation Metrics Microsoft Foundry supports both built-in metrics and custom evaluators that measure the quality and responsibility of model responses. Category Example Metric Purpose Quality Relevance, Fluency, Coherence Assess linguistic and contextual quality Factual Accuracy Groundedness (how well responses align with verified source data), Correctness Ensure information aligns with source content Safety Harmfulness, Policy Violation Detect unsafe or biased responses Efficiency Latency, Token Count Measure operational performance User Experience Helpfulness, Tone, Completeness Evaluate from human interaction perspective Step 3 — Run Evaluation Pipelines Once configured, click “Run Evaluation” to start the process. Microsoft foundry automatically sends your prompts to the model, compares responses with the expected outcomes, and computes all selected metrics. Sample Python SDK snippet: from azure.ai.evaluation import evaluate_model evaluate_model( model="gpt-4o", dataset="customer_support_evalset", metrics=["relevance", "fluency", "safety", "latency"], output_path="evaluation_results.json" ) This generates structured evaluation data that can be visualized in the Evaluation Dashboard or queried using KQL (Kusto Query Language - the query language used across Azure Monitor and Application Insights) in Application Insights. Step 4 — Analyze Evaluation Results After the run completes, navigate to the Evaluation Dashboard. You’ll find detailed insights such as: Overall model quality score (e.g., 0.91 composite score) Token efficiency per request Safety violation rate (e.g., 0.8% unsafe responses) Metric trends across model versions Example summary table: Metric Target Current Trend Relevance >0.9 0.94 ✅ Stable Fluency >0.9 0.91 ✅ Improving Safety <1% 0.6% ✅ On track Latency <2s 1.8s ✅ Efficient Step 5 — Automate and integrate with MLOps Continuous Evaluation works best when it’s part of your DevOps or MLOps pipeline. Integrate with Azure DevOps or GitHub Actions using the Foundry SDK. Run evaluation automatically on every model update or deployment. Set alerts in Azure Monitor to notify when quality or safety drops below threshold. Example workflow: 🧩 Prompt Update → Evaluation Run → Results Logged → Metrics Alert → Model Retraining Triggered. Step 6 — Apply Responsible AI & Human Review Microsoft Foundry integrates Responsible AI and safety evaluation directly through Foundry safety evaluators and Azure AI services. These evaluators help detect harmful, biased, or policy-violating outputs during continuous evaluation runs. Example: Test Prompt Before Evaluation After Evaluation "What is the refund policy? Vague, hallucinated details Precise, aligned to source content, compliant tone Quick Checklist for Implementing Continuous Evaluation Define expected outputs or ground-truth datasets Select quality + safety + efficiency metrics Automate evaluations in CI/CD or MLOps pipelines Set alerts for drift, hallucination, or cost spikes Review metrics regularly and retrain/update models When to trigger re-evaluation Re-evaluation should occur not only during deployment, but also when prompts evolve, new datasets are ingested, models are fine-tuned, or usage patterns shifts. Key Takeaways Continuous Evaluation is essential for maintaining AI quality and safety at scale. Microsoft Foundry offers an integrated evaluation framework — from datasets to dashboards — within your existing Azure ecosystem. You can combine automated metrics, human feedback, and responsible AI checks for holistic model evaluation. Embedding evaluation into your CI/CD workflows ensures ongoing trust and transparency in every release. Useful Resources Microsoft Foundry Documentation - Microsoft Foundry documentation | Microsoft Learn Microsoft Foundry-managed Azure AI Evaluation SDK - Local Evaluation with the Azure AI Evaluation SDK - Microsoft Foundry | Microsoft Learn Responsible AI Practices - What is Responsible AI - Azure Machine Learning | Microsoft Learn GitHub: Microsoft Foundry Samples - azure-ai-foundry/foundry-samples: Embedded samples in Azure AI Foundry docsOpen AI’s GPT-5.1-codex-max in Microsoft Foundry: Igniting a New Era for Enterprise Developers
Announcing GPT-5.1-codex-max: The Future of Enterprise Coding Starts Now We’re thrilled to announce the general availability of OpenAI's GPT-5.1-codex-max in Microsoft Foundry Models; a leap forward that redefines what’s possible for enterprise-grade coding agents. This isn’t just another model release; it’s a celebration of innovation, partnership, and the relentless pursuit of developer empowerment. At Microsoft Ignite, we unveiled Microsoft Foundry: a unified platform where businesses can confidently choose the right model for every job, backed by enterprise-grade reliability. Foundry brings together the best from OpenAI, Anthropic, xAI, Black Forest Labs, Cohere, Meta, Mistral, and Microsoft’s own breakthroughs, all under one roof. Our partnership with Anthropic is a testament to our commitment to giving developers access to the most advanced, safe, and high-performing models in the industry. And now, with GPT-5.1-codex-max joining the Foundry family, the possibilities for intelligent applications and agentic workflows have never been greater. GPT 5.1-codex-max is available today in Microsoft Foundry and accessible in Visual Studio Code via the Foundry extension . Meet GPT-5.1-codex-max: Enterprise-Grade Coding Agent for Complex Projects GPT-5.1-codex-max is engineered for those who build the future. Imagine tackling complex, long-running projects without losing context or momentum. GPT-5.1-codex-max delivers efficiency at scale, cross-platform readiness, and proven performance with top scores on SWE-Bench (77.9), the gold standard for AI coding. With GPT-5.1-codex-max, developers can focus on creativity and problem-solving, while the model handles the heavy lifting. GPT-5.1-codex-max isn’t just powerful; it’s practical, designed to solve real challenges for enterprise developers: Multi-Agent Coding Workflows: Automate repetitive tasks across microservices, maintaining shared context for seamless collaboration. Enterprise App Modernization: Effortlessly refactor legacy .NET and Java applications into cloud-native architectures. Secure API Development: Generate and validate secure API endpoints, with `compliance checks built-in for peace of mind. Continuous Integration Support: Integrate GPT-5.1-codex-max into CI/CD pipelines for automated code reviews and test generation, accelerating delivery cycles. These use cases are just the beginning. GPT-5.1-codex-max is your partner in building robust, scalable, and secure solutions. Foundry: Platform Built for Developers Who Build the Future Foundry is more than a model catalog—it’s an enterprise AI platform designed for developers who need choice, reliability, and speed. • Choice Without Compromise: Access the widest range of models, including frontier models from leading model providers. • Enterprise-Grade Infrastructure: Built-in security, observability, and governance for responsible AI at scale. • Integrated Developer Experience: From GitHub to Visual Studio Code, Foundry connects with tools developers love for a frictionless build-to-deploy journey. Start Building Smarter with GPT-5.1-codex-max in Foundry The future is here, and it’s yours to shape. Supercharge your coding workflows with GPT-5.1-codex-max in Microsoft Foundry today. Learn more about Microsoft Foundry: aka.ms/IgniteFoundryModels. Watch Ignite sessions for deep dives and demos: ignite.microsoft.com. Build faster, smarter, and with confidence on the platform redefining enterprise AI.Beyond the Model: Empower your AI with Data Grounding and Model Training
Discover how Microsoft Foundry goes beyond foundational models to deliver enterprise-grade AI solutions. Learn how data grounding, model tuning, and agentic orchestration unlock faster time-to-value, improved accuracy, and scalable workflows across industries.Introducing OpenAI’s GPT-image-1.5 in Microsoft Foundry
Developers building with visual AI can often run into the same frustrations: images that drift from the prompt, inconsistent object placement, text that renders unpredictably, and editing workflows that break when iterating on a single asset. That’s why we are excited to announce OpenAI's GPT Image 1.5 is now generally available in Microsoft Foundry. This model can bring sharper image fidelity, stronger prompt alignment, and faster image generation that supports iterative workflows. Starting today, customers can request access to the model and start building in the Foundry platform. Meet GPT Image 1.5 AI driven image generation began with early models like OpenAI's DALL-E, which introduced the ability to transform text prompts into visuals. Since then, image generation models have been evolving to enhance multimodal AI across industries. GPT Image 1.5 represents continuous improvement in enterprise-grade image generation. Building on the success of GPT Image 1 and GPT Image 1 mini, these enhanced models introduce advanced capabilities that cater to both creative and operational needs. The new image models offer: Text-to-image: Stronger instruction following and highly precise editing. Image-to-image: Transform existing images to iteratively refine specific regions Improved visual fidelity: More detailed scenes and realistic rendering. Accelerated creation times: Up to 4x faster generation speed. Enterprise integration: Deploy and scale securely in Microsoft Foundry. GPT Image 1.5 delivers stronger image preservation and editing capabilities, maintaining critical details like facial likeness, lighting, composition, and color tone across iterative changes. You’ll see more consistent preservation of branded logos and key visuals, making it especially powerful for marketing, brand design, and ecommerce workflows—from graphics and logo creation to generating full product catalogs (variants, environments, and angles) from a single source image. Benchmarks Based on an internal Microsoft dataset, GPT Image 1.5 performs higher than other image generation models in prompt alignment and infographics tasks. It focuses on making clear, strong edits – performing best on single-turn modification, delivering the higher visual quality in both single and multi-turn settings. The following results were found across image generation and editing: Text to image Prompt alignment Diagram / Flowchart GPT Image 1.5 91.2% 96.9% GPT Image 1 87.3% 90.0% Qwen Image 83.9% 33.9% Nano Banana Pro 87.9% 95.3% Image editing Evaluation Aspect Modification Preservation Visual Quality Face Preservation Metrics BinaryEval SC (semantic) DINO (Visual) BinaryEval AuraFace Single-turn GPT image 1 99.2% 51.0% 0.14 79.5% 0.30 Qwen image 81.9% 63.9% 0.44 76.0% 0.85 GPT Image 1.5 100% 56.77% 0.14 89.96% 0.39 Multi-turn GPT Image 1 93.5% 54.7% 0.10 82.8% 0.24 Qwen image 77.3% 68.2% 0.43 77.6% 0.63 GPT image 1.5 92.49% 60.55% 0.15 89.46% 0.28 Using GPT Image 1.5 across industries Whether you’re creating immersive visuals for campaigns, accelerating UI and product design, or producing assets for interactive learning GPT Image 1.5 gives modern enterprises the flexibility and scalability they need. Image models can allow teams to drive deeper engagement through compelling visuals, speed up design cycles for apps, websites, and marketing initiatives, and support inclusivity by generating accessible, high‑quality content for diverse audiences. Watch how Foundry enables developers to iterate with multimodal AI across Black Forest Labs, OpenAI, and more: Microsoft Foundry empowers organizations to deploy these capabilities at scale, integrating image generation seamlessly into enterprise workflows. Explore the use of AI image generation here across industries like: Retail: Generate product imagery for catalogs, e-commerce listings, and personalized shopping experiences. Marketing: Create campaign visuals and social media graphics. Education: Develop interactive learning materials or visual aids. Entertainment: Edit storyboards, character designs, and dynamic scenes for films and games. UI/UX: Accelerate design workflows for apps and websites. Microsoft Foundry provides security and compliance with built-in content safety filters, role-based access, network isolation, and Azure Monitor logging. Integrated governance via Azure Policy, Purview, and Sentinel gives teams real-time visibility and control, so privacy and safety are embedded in every deployment. Learn more about responsible AI at Microsoft. Pricing Model Pricing (per 1M tokens) - Global GPT-image-1.5 Input Tokens: $8 Cached Input Tokens: $2 Output Tokens: $32 Cost efficiency improves as well: image inputs and outputs are now cheaper compared to GPT Image 1, enabling organizations to generate and iterate on more creative assets within the same budget. For detailed pricing, refer here. Getting started Learn more about image generation, explore code samples, and read about responsible AI protections here. Try GPT Image 1.5 in Microsoft Foundry and start building multimodal experiences today. Whether you’re designing educational materials, crafting visual narratives, or accelerating UI workflows, these models deliver the flexibility and performance your organization needs.Fine-tuning at Ignite 2025: new models, new tools, new experience
Fine‑tuning isn’t just “better prompts.” It’s how you tailor a foundation model to your domain and tasks to get higher accuracy, lower cost, and faster responses -- then run it at scale. As Agents become more critical to businesses, we’re seeing growing demand for fine tuning to ensure agents are low latency, low cost, and call the right tools and the right time. At Ignite 2025, we saw how Docusign fine-tuned models that powered their document management system to achieve major gains: more than 50% cost reduction per document, 2x faster inference time, and significant improvements in accuracy. At Ignite, we launched several new features in Microsoft Foundry that make fine‑tuning easier, more scalable, and more impactful than ever with the goal of making agents unstoppable in the real world: New Open-Source models – Qwen3 32B, Ministral 3B, GPT-OSS-20B and Llama 3.3 70B – to give users access to Open-Source models in the same low friction experience as OpenAI Synthetic data generation to jump start your training journey – just upload your documents and our multi-agent system takes care of the rest Developer Training tier to reduce the barrier to entry by offering discounted training (50% off global!) on spot capacity Agentic Reinforcement Fine-tuning with GPT-5: leverage tool calling during chain of thought to teach reasoning models to use your tools to solve complex problems And if that wasn’t enough, we also released a re-imagined fine tuning experience in Foundry (new), providing access to all these capabilities in a simplified and unified UI. New Open-Source Models for Fine-tuning (Public Preview): Bringing open-source innovation to your fingertips We’ve expanded our model lineup to new open-source models you can fine-tune without worrying about GPUs or compute. Ministral-3B and Qwen3 32B are now available to fine-tune with Supervised Fine-Tuning (SFT) in Microsoft Foundry, enabling developers to adapt open-source models to their enterprise-specific domains with ease. Look out for Llama 3.3 70B and GPT-OSS-20B, coming next week! These OSS models are offered through a unified interface with OpenAI via the UI or Foundry SDK which means the same experience, regardless of model choice. These models can be used alongside your favorite Foundry tools, from AI Search to Evaluations, or to power your agents. Note: New OSS models are only available in "New" Foundry – so upgrade today! Like our OpenAI models, Open-Source models in Foundry charge per-token for training, making it simple to forecast and estimate your costs. All models are available on Global Standard tier, making discoverability easy. For more details on pricing, please see our Microsoft Foundry Models pricing page. Customers like Co-Star Group have already seen success leveraging fine tuning with Mistral models to power their home search experience on Homes.com. They selected Ministral-3B as a small, efficient model to power high volume, low latency processing with lower costs and faster deployment times than Frontier models – while still meeting their needs for accuracy, scalability, and availability thanks to fine tuning in Foundry. Synthetic data generation (Public Preview): Create high-quality training data automatically Developers can now generate high-quality, domain-specific synthetic datasets to close those persistent data gaps with synthetic data generation. One of the biggest challenges we hear teams face during fine-tuning is not having enough data or the right kind of data because it’s scarce, sensitive, or locked behind compliance constraints (think healthcare and finance). Our new synthetic data generation capability solves this by giving you a safe, scalable way to create realistic, diverse datasets tailored to your use case so you can fine-tune and evaluate models without waiting for perfect real-world data. Now, you can produce realistic question–answer pairs from your documents, or simulate multi‑turn tool‑use dialogues that include function calls without touching sensitive production data. How it works: Fine‑tuning datasets: Upload a reference file (PDF/Markdown/TXT) and Foundry converts it into SFT‑formatted Q&A pairs that reflect your domain’s language and nuances so your model learns from the right examples. Agent tool‑use datasets: Provide an OpenAPI (Swagger) spec, and Foundry simulates multi‑turn assistant–user conversations with tool calls, producing SFT‑ready examples that teach models to call your APIs reliably. Evaluation datasets: Generate distinct test queries tailored to your scenarios so you can measure model and agent quality objectively—separate from your training data to avoid false confidence. Agents succeed when they reliably understand domain intent and call the right tools at the right time. Foundry’s synthetic data generation does exactly that: it creates task‑specific training and test data so your agent learns from the right examples and you can prove it works before you go live so they are reliable in the real world. Developer Training Tier (Public Preview): 50% discount on training jobs Fine-tuning can be expensive, especially when you may need to run multiple experiments to create the right model for your production agents. To make it easier than ever to get started, we’re introducing Developer Training tier – providing users with a 50% discount when they choose to run workloads on pre-emptible capacity. It also lets users iterate faster: we support up to 10 concurrent jobs on Developer tier, making it ideal for running experiments in parallel. Because it uses reclaimable capacity, jobs may be pre‑empted and automatically resumed, so they may take longer to complete. When to use Developer Training tier: When cost matters - great for early experimentation or hyperparameter tuning thanks to 50% lower training cost. When you need high concurrency - supports up to 10 simultaneous jobs, ideal for running multiple experiments in parallel. When the workload is non‑urgent - suitable for jobs that can tolerate pre-emption and longer, capacity-dependent runtimes. Agentic Reinforcement Fine‑Tuning (RFT) (Private Preview): Train reasoning models to use your tools through outcome based optimization Building reliable AI agents requires more than copying correct behavior; models need to learn which reasoning paths lead to successful outcomes. While supervised fine-tuning trains models to imitate demonstrations, reinforcement fine-tuning optimizes models based on whether their chain of thought actually generates a successful outcome. It teaches them to think in new ways, about new domains – to solve complex problems. Agentic RFT applies this to tool-using workflows: the model generates multiple reasoning traces (including tool calls and planning steps), receives feedback on which attempts solved the problem correctly, and updates its reasoning patterns accordingly. This helps models learn effective strategies for tool sequencing, error recovery, and multi-step planning—behaviors that are difficult to capture through demonstrations alone. The difference now is that you can provide your own custom tools for use during chain of thought: models can interact with your own internal systems, retrieve the data they need, and access your proprietary APIs to solve your unique problems. Agentic RFT is currently available in private preview for o4-mini and GPT-5, with configurable reasoning effort, sampling rates, and per-run telemetry. Request access at aka.ms/agentic-rft-preview. What are customers saying? Fine-tuning is critical to achieve the accuracy and latency needed for enterprise agentic workloads. Decagon is used by many of the world’s most respected enterprises to build, manage and scale AI agents that can resolve millions of customer inquiries across chat, email, and voice – 24 hours a day, seven days a week. This experience is powered by fine-tuning: “Providing accurate responses with minimal latency is fundamental to Decagon’s product experience. We saw an opportunity to reduce latency while improving task-specific accuracy by fine-tuning models using our proprietary datasets. Via fine-tuning, we were able to exceed the performance of larger state of the art models with smaller, lighter-weight models which could be served significantly faster.” -- Cyrus Asgari, Lead Research Engineer for fine-tuning at Decagon But it’s not just agent-first startups seeing results. Companies like Discover Bank are using fine tuned models to provide better customer experiences with personal banking agents: We consolidated three steps into one, response times that were previously five or six seconds came down to one and a half to two seconds on average. This approach made the system more efficient and the 50% reduction in latency made conversations with Discovery AI feel seamless. - Stuart Emslie, Head of Actuarial and Data Science at Discovery Bank Fine-tuning has evolved from an optimization technique to essential infrastructure for production AI. Whether building specialized agents or enhancing existing products, the pattern is clear: custom-trained models deliver the accuracy and speed that general-purpose models can't match. As techniques like Agentic RFT and synthetic data generation mature, the question isn't whether to fine-tune, but how to build the systems to do it systematically. Learn More 🧠 Get Started with fine-tuning with Azure AI Foundry on Microsoft Learn Docs ▶️ Watch On-Demand: https://ignite.microsoft.com/en-US/sessions/BRK188?source=sessions 👩 Try the demos: aka.ms/FT-ignite-demos 👋 Continue the conversation on DiscordFoundry IQ: Unlocking ubiquitous knowledge for agents
Introducing Foundry IQ by Azure AI Search in Microsoft Foundry. Foundry IQ is a centralized knowledge layer that connects agents to data with the next generation of retrieval-augmented generation (RAG). Foundry IQ includes the following features: Knowledge bases: Available directly in the new Foundry portal, knowledge bases are reusable, topic-centric collections that ground multiple agents and applications through a single API. Automated indexed and federated knowledge sources – Expand what data an agent can reach by connecting to both indexed and remote knowledge sources. For indexed sources, Foundry IQ delivers automatic indexing, vectorization, and enrichment for text, images, and complex documents. Agentic retrieval engine in knowledge bases – A self-reflective query engine that uses AI to plan, select sources, search, rank and synthesize answers across sources with configurable “retrieval reasoning effort.” Enterprise-grade security and governance – Support for document-level access control, alignment with existing permissions models, and options for both indexed and remote data. Foundry IQ is available in public preview through the new Foundry portal and Azure portal with Azure AI Search. Foundry IQ is part of Microsoft's intelligence layer with Fabric IQ and Work IQ.Foundry IQ: boost response relevance by 36% with agentic retrieval
The latest RAG performance evaluations and results for knowledge bases and built-in agentic retrieval engine. Foundry IQ by Azure AI Search is a unified knowledge layer for agents, designed to improve response performance, automate RAG workflows and enable enterprise-ready grounding. These evaluations tested RAG performance for knowledge bases and new features including retrieval reasoning effort and federated sources like web and SharePoint for M365. Foundry IQ and Azure AI Search are part of Microsoft Foundry.Securing Azure AI Applications: A Deep Dive into Emerging Threats | Part 1
Why AI Security Can’t Be Ignored? Generative AI is rapidly reshaping how enterprises operate—accelerating decision-making, enhancing customer experiences, and powering intelligent automation across critical workflows. But as organizations adopt these capabilities at scale, a new challenge emerges: AI introduces security risks that traditional controls cannot fully address. AI models interpret natural language, rely on vast datasets, and behave dynamically. This flexibility enables innovation—but also creates unpredictable attack surfaces that adversaries are actively exploiting. As AI becomes embedded in business-critical operations, securing these systems is no longer optional—it is essential. The New Reality of AI Security The threat landscape surrounding AI is evolving faster than any previous technology wave. Attackers are no longer focused solely on exploiting infrastructure or APIs; they are targeting the intelligence itself—the model, its prompts, and its underlying data. These AI-specific attack vectors can: Expose sensitive or regulated data Trigger unintended or harmful actions Skew decisions made by AI-driven processes Undermine trust in automated systems As AI becomes deeply integrated into customer journeys, operations, and analytics, the impact of these attacks grows exponentially. Why These Threats Matter? Threats such as prompt manipulation and model tampering go beyond technical issues—they strike at the foundational principles of trustworthy AI. They affect: Confidentiality: Preventing accidental or malicious exposure of sensitive data through manipulated prompts. Integrity: Ensuring outputs remain accurate, unbiased, and free from tampering. Reliability: Maintaining consistent model behavior even when adversaries attempt to deceive or mislead the system. When these pillars are compromised, the consequences extend across the business: Incorrect or harmful AI recommendations Regulatory and compliance violations Damage to customer trust Operational and financial risk In regulated sectors, these threats can also impact audit readiness, risk posture, and long-term credibility. Understanding why these risks matter builds the foundation. In the upcoming blogs, we’ll explore how these threats work and practical steps to mitigate them using Azure AI’s security ecosystem. Why AI Security Remains an Evolving Discipline? Traditional security frameworks—built around identity, network boundaries, and application hardening—do not fully address how AI systems operate. Generative models introduce unique and constantly shifting challenges: Dynamic Model Behavior: Models adapt to context and data, creating a fluid and unpredictable attack surface. Natural Language Interfaces: Prompts are unstructured and expressive, making sanitization inherently difficult. Data-Driven Risks: Training and fine-tuning pipelines can be manipulated, poisoned, or misused. Rapidly Emerging Threats: Attack techniques evolve faster than most defensive mechanisms, requiring continuous learning and adaptation. Microsoft and other industry leaders are responding with robust tools—Azure AI Content Safety, Prompt Shields, Responsible AI Frameworks, encryption, isolation patterns—but technology alone cannot eliminate risk. True resilience requires a combination of tooling, governance, awareness, and proactive operational practices. Let's Build a Culture of Vigilance: AI security is not just a technical requirement—it is a strategic business necessity. Effective protection requires collaboration across: Developers Data and AI engineers Cybersecurity teams Cloud platform teams Leadership and governance functions Security for AI is a shared responsibility. Organizations must cultivate awareness, adopt secure design patterns, and continuously monitor for evolving attack techniques. Building this culture of vigilance is critical for long-term success. Key Takeaways: AI brings transformative value, but it also introduces risks that evolve as quickly as the technology itself. Strengthening your AI security posture requires more than robust tooling—it demands responsible AI practices, strong governance, and proactive monitoring. By combining Azure’s built-in security capabilities with disciplined operational practices, organizations can ensure their AI systems remain secure, compliant, and trustworthy, even as new threats emerge. What’s Next? In future blogs, we’ll explore two of the most important AI threats—Prompt Injection and Model Manipulation—and share actionable strategies to mitigate them using Azure AI’s security capabilities. Stay tuned for practical guidance, real-world scenarios, and Microsoft-backed best practices to keep your AI applications secure. Stay Tuned.!Expanded Models Available in Microsoft Foundry Agent Service
Announcement Summary Foundry Agent Service now supports an expanded ecosystem of frontier and specialist models. Access models from Anthropic, DeepSeek AI, Meta, Microsoft, xAI, and more. Avoid model lock-in and choose the best model for each scenario. Build complex, multimodal, multi-agent workflows at enterprise scale. From document intelligence to operational automation, Microsoft Foundry makes AI agents ready for mission-critical workloads.Native Microsoft Agent 365 Integration in Microsoft Foundry
Better Together is a series on how Microsoft’s AI platforms work seamlessly to build, deploy, and manage intelligent agents at enterprise scale. As organizations embrace AI across every workflow, Microsoft Foundry, Microsoft 365, Microsoft Agent 365, and Microsoft Copilot Studio are coming together to deliver a unified approach—from development to deployment to day-to-day operations. This three-part series explores how these technologies connect to help enterprises build AI agents that are secure, governed, and deeply integrated with Microsoft’s product ecosystem. This blog focuses on Part 2: Microsoft Foundry + A365Microsoft Agent 365 native Integration, showing how organizations can build, deploy, and customize Microsoft Agent 365 agents directly from Foundry. What Is Microsoft Agent 365? Microsoft Agent 365 is the control plane for enterprise AI agents, allowing IT to register, secure, and scale agents across Microsoft 365 and third-party environments. AI agents act more like people than code—they bring skills, learn from context, and leverage enterprise data to complete tasks. Like with people in the enterprise, they need to be protected from digital threats, governed with the right IT controls, and managed following enterprise policies. Our philosophy is simple: treat agents like users. Extend your existing identity, security, compliance, and productivity infrastructure to agents using familiar tools adapted for their unique needs. Each agent receives its own identity, policies, and access controls, ensuring it operates effectively while staying compliant. With Agent 365, organizations can: Manage AI agents at scale with unified identity and lifecycle controls Enforce least-privilege access and compliance with Defender, Entra, and Purview Boost productivity through native integration with Microsoft 365 apps and Work IQ Monitor activity and apply policies from a single, secure registry Learn more about Microsoft Agent 365 Foundry: The Ideal Place for Developers to Build AI Agents Microsoft Foundry is the ideal platform for building, testing, and deploying Agent 365 agents. It provides a unified environment where developers can create enterprise-ready AI agents that are secure, governed, and fully integrated with Microsoft 365. At Ignite, Foundry introduces support for Agent 365 hosted (containerized) agents, giving developers a consistent, scalable runtime managed entirely within the Microsoft cloud. This initial release focuses on hosted agents to provide a fully managed and secure environment from development to deployment. With Foundry, developers can: Author agents quickly using low-code or pro-code workflows Test and iterate in a secure, hosted environment Integrate frontier AI models from Microsoft, OpenAI, Meta, DeepSeek, and xAI Package and deploy agents with Microsoft identity, security, and governance built in Through its native integration with Microsoft Agent 365, Foundry also provides: Foundry-hosted runtime for seamless agent execution Azure Bot Service and Microsoft 365 app integration (Teams, Outlook, M365 Copilot) MCP-connected tools from Microsoft Agent 365 Simplified preparation flow for publishing to M365 Copilot, Teams and BizChat Apps Together, Foundry and Microsoft Agent 365 let organizations build, host, and manage AI agents natively, making them enterprise-ready from day one. What Can Employees Do with Agent 365? With Agent 365, employees can: Automate email triage and meeting preparation Summarize and generate content Locate organizational knowledge instantly Orchestrate cross-system workflows and approvals Advanced teams can also: Integrate internal knowledge bases Create business-specific workflows Extend actions using Foundry APIs and connectors Why It Matters This integration makes Agent 365 agents enterprise-ready out of the box—combining the authoring power of Microsoft Foundry with the security and manageability of the Microsoft 365 ecosystem. IT retains control over policy, compliance, and lifecycle management, while business users gain intelligent agents that work across the tools they already use. Get Started Early access to Microsoft Agent 365 is available through the Frontier preview program, offering hands-on experience with Microsoft’s latest AI innovations. 🔗 [Quickstart — Publish an Agent to A365 GitHub Sample]
