Log inSign up
user avatar
DevOps on Azure
@AzureDevOps
Deliver innovation faster with simple, reliable tools for continuous delivery. Official handle of Azure DevOps at Microsoft
Azure ☁️
Joined September 2013
361
Following
95.7K
Followers
RepliesMedia
  • user avatar
    DevOps on Azure
    @AzureDevOps
    Jul 31
    AI agents don’t become “production” by vibes alone. 😎 This course shows the real path — MAF services on ports, Foundry Hosted Agents, smoke‑test CI gates, capability hosts for data sovereignty, toolbox governance, and A2A interop. 👉 Read: buff.ly/znxxDKJ
    Landing page hero promoting a Microsoft open-source course "Building AI Agents from Zero to Production," summarizing the agent lifecycle with five steps—design, develop, evaluate, deploy, govern—and a highlighted "Evaluate & observe" stage.
  • user avatar
    DevOps on Azure
    @AzureDevOps
    Jul 29
    How do you build Microsoft 365 Copilot agents that are useful, grounded, extensible, and measurable? Join The Microsoft 365 Copilot Agent’s Playbook, a 4-part livestream series on declarative agents, WorkIQ, MCP apps, and Copilot Evals. Register: aka.ms/M365AgentsPlay…
    Microsoft Reactor promo for "The Microsoft 365 Copilot Agent’s Playbook Series" — virtual sessions Aug 18–Sept 8, Tuesdays 9–10 AM PT; register at aka.ms/M365AgentsPlaybook; eight speaker headshots shown.
  • user avatar
    DevOps on Azure
    @AzureDevOps
    Jul 29
    Want to know how your Copilot Studio agent behaves under real pressure? Locust + Azure Load Testing = scripted swarms, scalable test engines, and dashboards that expose every bottleneck. Perfect for tuning prompts, flows, and backends before launch. 👉 buff.ly/ypKYsVQ
    Load-testing diagram: Locust virtual users send concurrent HTTP/WebSocket requests via Direct Line to a Copilot Studio agent, which orchestrates model, knowledge, tools and connected agents to reach back-end services like Power Automate, connectors, APIs and MCP servers.
  • user avatar
    DevOps on Azure
    @AzureDevOps
    Jul 28
    💸⚙️ DevOps folks: tokens = cost. Agentic AI can fire off dozens of model calls per task — planning, tools, retries, summarization. FinOps for AI = compression, caching, routing, and metering so frontier models only run when they matter. 📚 Blog: buff.ly/eySQCJM
    Multi-agent coding evaluation engine showing Planner, Coding, Tool, and Reviewer agents iterating on tasks while a Token Optimization Core (context compression, caching, model routing, short-term memory) reduces tokens and routes work to small or large LLMs; test cases and evaluation metrics shown.