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transitioning pipelines to a devsecops pipeline design

  Traditional DevOps focuses on speed and reliability but often treats security as an afterthought — vulnerabilities are caught too late in production. DevSecOps integrates security at every stage of the pipeline. Here are the critical additions: The "Shift Left" Principle Instead of finding security issues after deployment, DevSecOps catches them early : Pre-commit : Scan for secrets before code is pushed Build time : SAST analysis, dependency checks, container scanning Pre-deploy : Verify signatures, compliance gates, IaC security Runtime : Intrusion detection, vulnerability monitoring Post-deploy : DAST testing, regression checks # DevSecOps vs DevOps Pipelines in GitHub Actions ## Core Differences ### Traditional DevOps Pipeline - ** Focus ** : Speed and reliability - ** Security ** : Added at the end (security testing after deployment) - ** Approach ** : "Shift right" - security concerns are addressed late ### DevSecOps Pipeline - ** Focus ...
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github actions - variable and references

In github actions, we often generate a pipeline variable and then re-use it elsewhere in our pipeline, the example here shows how can we do just that:-  name : Manual Deploy on :   workflow_dispatch :     inputs :       environment :         description : ' Deployment environment '         required : true         default : ' staging '         type : choice         options :           - staging           - production       version :         description : ' Version to deploy '         required : true         type : string jobs :   build :     runs-on : ubuntu-latest     outputs :       image-tag : ${{ steps.meta.outputs.tags }}     steps :       - id : met...
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Azure foundry agent_framework default tool

Today we are going to look at the tool available by default in agent_framework pypi package.  A2A tool -  get_a2a_tool Web search tool -  get_web_search_tool File search tool -  get_file_search_tool Azure AI search tool -  get_azure_ai_search_tool bing custom search tool -  get_bing_custom_search_tool bing grounding tool -  get_bing_grounding_tool brower automation tool -  get_browser_automation_tool code interpreter tool -  get_code_interpreter_tool computer use tool -  get_computer_use_tool fabric tool -  get_fabric_tool image generation tool -  get_image_generation_tool mcp tool -  get_mcp_tool memory tool -   get_memory_search_tool shell tool -  get_shell_tool To use these tool here are some code snippet for illustration only :- import asyncio import os from agent_framework import Agent from agent_framework . foundry import FoundryChatClient from azure . identity import AzureCliCredential asyn...
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Github copilot SDK - wanted to write your own coding agent?

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You can now write your own coding editor using copilot SDK - a production ready, mature and well tested framework. For more info, try visiting https://github.com/github/copilot-sdk And the best part with this is that we have some sample implementation code here:- https://github.com/microsoft-foundry/foundry-samples/tree/main/samples/python/hosted-agents/bring-your-own/activity/github-copilot/src/github-copilot-activity And we have some docs to go with it here:- https://learn.microsoft.com/en-us/azure/foundry/how-to/develop/use-microsoft-foundry-skill?tabs=vscode
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mcp tips microsoft agent_framework tool

Some tips when working with agent_framework Approve mode Did you know we can use get away with those manual approval everytime an agent call a remote tool with " approval_mode=never_required " - here is an example code snippet that allows us to do this. @ tool ( description = " List files in a directory. " , approval_mode = " never_require " ) def list_files ( directory : str ) -> list [ str ]:     """ List files in a directory. """     try :         return os . listdir ( directory )     except Exception as e :         return [ f "Error listing files in { directory } : { e } " ]     Azure Foundry Toolkit 
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Azure foundry toolkit - essential tool for AI developer

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Have you tried Azure Foundry Toolkit - it is an essential toolki that allow us to develop, evaluate model and deploy Azure Agentic Framework app to Azure cloud. All that capabilities into your vscode. 
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Azure foundry file search - setup and deploying as a remote agent (not ephemeral agent)

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 File search allow us to feed information into our model  In this implementation we are deploying agent in Azure Foundry and it uses chatgpt-5-mini. I created the file search manually. You agent can only have 1 index.  File search is not RAG.  With RAG you use Azure AI Search. File search is pretty generic. It requires basic setup where you don't necessary have to create a storage account manually to host your file.  As you can see here, I am uploading zippolock product info and it automatically create an index and embedd the info for me. There are limits to the file that you will be uploading.  And then I save this as an agent as shown here :- Ensuring we have the right pypi dependencies dependencies = [      "agent-framework>=1.13.0",      "azure-ai-projects", # Main Azure Foundry SDK      "azure-identity", # For authentication ] And then we can use the following code to query what is zippolock. The magic is...
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