Using Claude Opus 4.6 in Github Copilot
The model selection in Github Copilot got richer with the addition of Claude Opus 4.6. The Model capability along with the addition of agents makes it a powerful combination to build complex code which requires many hours or days. Claude Opus 4.6 is better in coding skills as compared to the previous models. It also plans more carefully, performs more reliably in larger codebases, and has better code review as well as debugging skills to catch its own mistakes. In my current experiment, I used it multiple times to review its own code and while it took time (understandably) to get familiar with the code base. After that initial effort on the evaluation, the suggestions for fixes/improvements were on dot and often even better than a human reviewer (me in this case). Opus 4.6 also can run agentic tasks for longer. Following the release of the model, Anthropic published a paper on using Opus 4.6 to build C Compiler with a team of parallel Claudes. The compiler was built by 16 agents from scratch to get a Rust-based C compiler which was capable of compiling the Linux kernel. This is an interesting paper (shared in resources). Using Claude Opus 4.6 in Agentic Mode In less than an hour, I built a document analyzer to analyse the content, extract insights, build knowledge graphs and summarize elements. The code was built using Claude Opus 4.6 alongwith Claude Agents in Visual Studio Code. The initial prompt built the code and in the next hour after a few more interactions - unit tests were added and the UI worked as expected specifically for rendering the graphs. In the second phase, I converted the capabilities into Agents with tools and skills making the codebase Agentic. All this was done in Visual Studio using Github Copilot. Adding the complexity of Agentic execution was staggered across phases but the coding agent may well have built it right in the first instance with detailed specifications and instructions. The Agent could also fix UI requirements and problems in graph rendering from the snapshot shared in the chat window. That along with the logging was sufficient to quickly get to an application which worked as expected. The final graph rendering used mermaid diagrams in javascript while the backend was in python. Knowledge Graph rendering using mermaid What are Agents? Agents perform complete coding tasks end-to-end. They understand your project, make changes across multiple files, run commands, and adapt based on the results. An agent runs in the local, background, cloud, or third-party mode. An agent takes a high-level task and it breaks the task down into steps. It executes those steps with tools and self-corrects on errors. Multiple agent sessions can run in parallel, each focused on a different task. On creating a new agent session, the previous session remains active and can be accessed between tasks via the agent sessions list. The Chat window in Visual Studio Code allows for changing the model and also the Agent Mode. The Agent mode can be local for Local Agents or run in the background or on Cloud. Additionally, Third Party Agents are also available for coding. In the snapshot below, the Claude Agent (Third Party Agent) is used. In this project Azure GPT 4.1 was used in the code to perform the document analysis but this can be changed to any model of choice. I also used the ‘Ask before edits” mode to track the command runs. Alternatively, the other option was to let the Agent run autonomously. Visual Studio Code - Models and Agent Mode The local Agentic mode was also a good option and I used it a few times specifically as it is not constrained by network connectivity. But when the local compute does not suffice, the cloud mode is the next best option. Background agents are CLI-based agents, such as Copilot CLI running in the background on your local machine. They operate autonomously in the editor and Background agents use Git worktrees to work in an isolated environment from your main workspace to prevent conflicts with your active work. How to get the model? The model is accessible to GitHub Copilot Pro/Pro+, business, and enterprise users. Opus 4.6 operates more reliably in large codebases, offering improved code review and debugging skills. The Fast mode for Claude Opus 4.6, rolled out in research preview, provides a high-speed option with output token delivery speeds up to 2.5 times faster while maintaining comparable capabilities to Opus 4.6. Resources https://www.anthropic.com/news/claude-opus-4-6 https://www.anthropic.com/engineering/building-c-compiler https://github.blog/changelog/2026-02-05-claude-opus-4-6-is-now-generally-available-for-github-copilot https://code.visualstudio.com/docs/copilot/agents/overview
Rename resources before migration
When replicating virtual machines to Azure, the resources are getting random names (disks and NIC's). Will there be an option to rename the resources before migrating to Azure? So all the resources are matching the naming convention of our customers or manged platform.Migrate VMs to Azure (Stack HCI)
Hi. I know this is a bit out of scope, but since Azure Stack HCI is still under the Azure programme, I ask it anyways. Would it be possible to extend the functionality of Azure Migrate to use an Azure Stack HCI cluster as destination for the VMs to migrate? Thanks and regards, Anders
Execute Azure Function using ADF activity and AAD Login
We are trying to execute an Azure Function (HTTP type) using Azure Data Factory (ADF). The Function App has the App Service Authentication set as "On" and the action to take when request is not authenticated is set as "Log in with Azure Active Directory". If we have this configuration in the Function App, is it possible to execute the Azure Function from ADF? We configured the Azure Function and Active Directory App by following this documentation: https://urldefense.proofpoint.com/v2/url?u=https-3A__nam06.safelinks.protection.outlook.com_-3Furl-3Dhttps-253A-252F-252Fdocs.microsoft.com-252Fen-2Dus-252Fazure-252Fapp-2Dservice-252Fconfigure-2Dauthentication-2Dprovider-2Daad-26data-3D04-257C01-257CFelipe.Rocha-2540microsoft.com-257C3e259ade08c043f75b0908d87b8bce47-257C72f988bf86f141af91ab2d7cd011db47-257C1-257C0-257C637395187680813277-257CUnknown-257CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0-253D-257C1000-26sdata-3DaLtqA0RLT-252BnOQgzRvpO3-252FBHymas6xDVnCjtNg-252BLTPn8-253D-26reserved-3D0&d=DwMFAw&c=eIGjsITfXP_y-DLLX0uEHXJvU8nOHrUK8IrwNKOtkVU&r=6PBUmFEROSKfIVB29NY06e8Dp-dqEFIr6V8Pn_TTogM&m=2xNcTOHFliLYWEEUfrAbIYlhonao8kIgcQ1S3dP6kyI&s=87sJjlggshqh-zIdTp1NjIVjCOkX3R0es972xbuL3A8&e= We aren’t using a web app and maybe that’s the confusing part when trying to follow the steps from the documentation. ADF is the one that needs to authenticate when trying to access/execute the function. Do you know anything about this type of configuration?
