AI & Copilot on Azure
20 articles from official documentation
Harnessing Microsoft Copilot in Foundry Agent Service: A Game Changer
Unlock the potential of Microsoft Copilot harness in Foundry Agent Service to streamline your agent execution. This managed system allows you to define agents declaratively, enhancing resilience for long-running tasks.
- →Leverage the managed execution system to simplify agent orchestration.
- →Utilize long-running resilience to ensure agents recover from interruptions.
Building Sovereign AI: A Seamless Edge-to-Cloud Foundation
Digital sovereignty is more critical than ever, especially when it comes to AI workloads. With Azure Local and Confidential VMs, you can control where your data is processed and ensure its security. This article dives into how to design a flexible architecture that meets these needs.
- →Leverage Azure Local for distributed infrastructure in customer-controlled environments.
- →Utilize Azure Confidential VMs for hardware-based workload isolation.
Navigating Open-Weight Model Risk in Azure Deployments
Open-weight models offer enterprises unprecedented flexibility, but they come with significant risks. Understanding the five categories of risk—like cybersecurity and bias—can help you manage these challenges effectively. Dive into how to evaluate and mitigate these risks in your deployments.
- →Understand the five risk categories: Cybersecurity, Data Protection, Content, Human-AI Interaction, and Bias.
- →Map evaluations to risks to identify relevant evidence for your deployment.
Building Scalable AI Workloads with Cloud-Native Infrastructure on Azure
AI infrastructure is all about enabling useful work, and cloud-native solutions are key to achieving that at scale. With the right compute, storage, and networking, you can focus on outcomes rather than infrastructure management. Dive in to learn how to optimize your AI deployments effectively.
- →Understand that AI infrastructure's value lies in the useful work it enables.
- →Leverage cloud-native solutions to allow compute, storage, and networking to adapt to workload changes.
Scaling Azure Infrastructure: Harnessing Open Collaboration for AI Workloads
Scaling Azure infrastructure is crucial for handling modern AI workloads efficiently. With innovations like the Azure Maia 300, you can achieve 40% more tokens per watt compared to traditional GPU systems. Dive in to discover how these advancements can transform your production environment.
- →Leverage Azure Maia 300 for improved inference efficiency, achieving 40% more tokens per watt.
- →Utilize Cobalt 200 for a 50% performance improvement and reduced latency in agentic workloads.
Transforming Infrastructure with AI: The Azure Advantage
AI is reshaping how we manage infrastructure, and Azure is at the forefront of this transformation. By leveraging AI for failure prediction and multi-agent workflows, you can proactively address issues before they impact your operations.
- →Implement 'Lean before AI' to simplify workflows and establish a robust data foundation.
- →Utilize multi-agent workflows to analyze signals and understand shifts in demand.
Microsoft's Leadership in Industrial AIoT: What It Means for Your Operations
Microsoft's recognition as a Leader in the 2026 Gartner Magic Quadrant for Global Industrial AIoT Platforms signals a significant shift in operational intelligence. With capabilities in Industrial AI and Physical AI, this platform helps you transition from mere automation to intelligent, learning operations.
- →Leverage Industrial AI to identify patterns and predict outcomes in operational data.
- →Utilize Physical AI for real-world applications that learn and improve over time.
Unlocking Voice Agents with Microsoft Foundry: Continuous Optimization in Action
Voice agents are now a core part of the Microsoft Foundry ecosystem, enabling teams to build and optimize agents seamlessly. With the Foundry Agent Service, you can leverage a model-agnostic foundation to enhance your voice interactions. Dive into how this can transform your agent development process.
- →Utilize the Foundry Agent Service for a model-agnostic foundation to build voice agents.
- →Implement the hill-climbing approach to continuously optimize your agents based on production traces.
Choosing the Right GPT-6 Model for Production AI Agents in Microsoft Foundry
Navigating the landscape of AI models can be daunting, but choosing the right one is crucial for your production needs. GPT-6 Astra excels in complex decision-making, while Sol and Luna offer efficiency for high-volume tasks. Discover how to leverage these models effectively.
- →Start with GPT-6 Astra for demanding tasks requiring advanced reasoning.
- →Leverage GPT-6 Sol for cost-effective, general-purpose intelligence.
Mastering AI Agent Governance: Measuring Value and ROI
Understanding AI agent governance is crucial for effective cost management and ROI measurement in your projects. With tools like Foundry, you can monitor costs and enforce token limits to keep expenses in check. Dive in to learn how to take control of your AI investments.
- →Understand AI agent governance to track ownership and access effectively.
- →Utilize Foundry's cost management capabilities to monitor costs across projects.
Adaptive AI in Research: Driving Discovery Beyond Benchmarks
Unlock the potential of agentic AI to tackle complex scientific challenges. Discover how Microsoft Discovery and CLIO outperform traditional methods by adapting strategies based on real-time evidence.
- →Leverage agentic AI to explore multiple hypotheses simultaneously.
- →Utilize Microsoft Discovery and CLIO for evidence-backed research trajectories.
Unlocking Enterprise AI Transformation with Azure's End-to-End Platform
Enterprise AI transformation is no longer a distant goal; it’s a necessity. Azure's comprehensive suite, including Microsoft Foundry and Microsoft Fabric, empowers organizations to seamlessly integrate AI across their operations. This article dives into how Azure's infrastructure supports this evolution.
- →Utilize Microsoft Foundry for broad model choice and essential AI tools.
- →Leverage Microsoft Fabric to unify analytics and data for better insights.
Unlocking GPT-6 Astra: The Future of Intelligent Work in Microsoft Foundry
GPT-6 Astra is here to revolutionize decision-making for complex tasks. With its ability to reason through challenges and produce actionable results, it’s a game changer for organizations. Dive into how this model integrates with Microsoft Foundry to enhance productivity.
- →Understand the deployment options: Standard for flexibility and Provisioned Throughput for guaranteed performance.
- →Leverage Astra's reasoning capabilities to tackle complex challenges and adapt plans dynamically.
Unlocking GPT-6 Astra: Transforming Workflows in Microsoft Foundry
GPT-6 Astra is here to revolutionize how organizations tackle complex tasks. This frontier model integrates seamlessly within Microsoft Foundry, offering dedicated processing capacity for consistent performance. Dive in to discover how it can enhance your operational efficiency.
- →Leverage GPT-6 Astra to tackle complex decision-making tasks effectively.
- →Choose between Standard and Provisioned Throughput deployment options based on your workload needs.
Optimizing AI Costs with Context Engineering in Azure
Cutting AI costs is crucial for any organization leveraging AI agents. Context engineering allows you to manage what enters the context window, ensuring agents only access necessary information, thus reducing token consumption significantly.
- →Implement context engineering to manage what enters the context window effectively.
- →Utilize Foundry IQ to decompose queries and reduce retrieval token costs.
Scaling Marketing Expertise with AI: Insights from Microsoft
Microsoft's marketing team leverages AI to enhance content creation and strategy. By using Microsoft Foundry, they integrate expert-defined standards into the workflow, ensuring quality and consistency across messaging.
- →Utilize Microsoft Foundry to build and manage enterprise AI applications.
- →Implement a repeatable workflow to identify content gaps before human review.
Mastering AI Cost Optimization in Azure: Strategies That Work
AI spending can spiral out of control if you're not careful. By leveraging tools like model routers and prompt caching, you can significantly reduce costs while maintaining performance. Dive into practical strategies that make a difference.
- →Leverage model routers to optimize request handling and reduce overhead.
- →Utilize fine-tuning to adapt smaller models for specific tasks, saving costs.
Transforming Legacy Systems: Microsoft Leads in AI-Augmented Code Modernization
Microsoft's leadership in the 2026 Gartner Magic Quadrant for AI-Augmented Code Modernization Tools highlights a game-changing approach to legacy system transformation. With GitHub Copilot at the helm, developers can automate repetitive tasks while maintaining control over upgrades and migrations to Azure.
- →Leverage GitHub Copilot to automate repetitive tasks during application upgrades.
- →Utilize the Modernize CLI for orchestrating assessment and planning across multiple applications.
Unlocking Value in Microsoft Databases: Reliability to AI Readiness
Microsoft Databases are more than just storage solutions; they are critical for modern applications. With options like Azure SQL Database simplifying modernization and Azure Cosmos DB offering global scale, understanding what customers value can transform your approach to database management.
- →Leverage SQL Server for mission-critical workloads to ensure trusted reliability.
- →Utilize Azure SQL Database for a seamless transition to cloud-scale SQL solutions.
Scaling Trillion-Token Workloads: Insights from AT&T and Microsoft
AT&T and Microsoft are pushing the boundaries of AI in telecommunications with Microsoft Foundry and AMD. By leveraging a multi open-model strategy, they can efficiently process vast amounts of telecom data, supporting trillions of tokens in a unified platform.
- →Utilize Microsoft Foundry Managed Compute for streamlined GPU access.
- →Leverage a multi open-model strategy to enhance flexibility in telecom data processing.
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