Connect Microsoft Graph API with Copilot Studio for email processing

 This article from Microsoft Q&A outlines how to use Microsoft Graph API to integrate Outlook email and calendar data into Copilot Studio agents. Key highlights include:

✅ Data Integration

• Use Microsoft Graph API to access Outlook data.
• Key endpoints:
  • /me/messages – Access emails.
  • /me/events – Access calendar events.
  • /me/mailFolders – Manage folders.

🛠️ Tools & APIs

• Microsoft Graph API – Primary API for accessing Microsoft 365 data.
• Microsoft Graph Connectors – Bring external data into Microsoft 365 and Copilot Studio.
• Power Platform Connectors – Use with Power Automate to bridge email data to Copilot Studio.

💡 Use Cases

• Email Summarization – Copilot extracts key insights from emails.
• Meeting Preparation – Generate agendas from calendar and email threads.
• Task Management – Create tasks based on email content.

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🧩 How to Call Microsoft Graph API from Copilot Agent 

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This guide explains how to:

• Use Power Automate to trigger Graph API calls.
• Set up OAuth 2.0 authentication using Azure AD.
• Send email data to Copilot Studio agents via HTTP actions.

https://learn.microsoft.com/en-us/answers/questions/2156030/integrating-outlook-as-a-knowledge-base-for-copilo

🤖 📄 How to Create a Document-insight Agent in Microsoft 365 Copilot

📚Microsoft 365 Copilot enables you to build custom agents that interact with users, retrieve knowledge from organizational data, and answer context-based questions. This guide covers how to create an agent that reads specified documents and provides accurate answers.

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✅ 1. Overview

The agent will:

• Read and process uploaded or linked documents.

• Answer user queries strictly based on document content.

• Support knowledge integration from SharePoint, OneDrive, and Teams.

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✅ 2. Steps to Create an Agent in Microsoft 365 Copilot

Step 1: Open Microsoft 365 Copilot

• Launch the Microsoft 365 Copilot app.

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Step 2: Access Agents

• Navigate to:
  Chat → Agents

• Click Create Agent or go to All Agents to explore templates.

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Step 3: Choose Creation Method

• From Scratch:
  Use Agent Builder with natural language instructions.

• Template:
  Select a pre-built option from Agent Store.

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Step 4: Configure the Agent

• Description:
  Define the purpose of the agent.
  Example:
  “This agent reads and indexes documents from connected knowledge sources and provides context-based answers.”

• Add Instructions:
  Use this prompt for agent behaviour:

✅ Agent Instruction Prompt

You are an intelligent Copilot agent designed to assist users by reading and understanding the content of specified documents and answering questions based on those documents.

Your objectives:
1. Read and process the provided documents thoroughly.
2. Break down documents into manageable sections for better retrieval.
3. When a user asks a question, search the documents to find the most relevant information.
4. Provide accurate, concise, and context-aware answers strictly based on the documents.
5. If the answer is not in the documents, respond with:
   "I could not find this information in the provided documents."
6. Never fabricate or use external knowledge.
7. Maintain a clear and professional tone.
8. Summarize for complex queries unless full details are requested.

Capabilities:
- Extract and parse text from PDFs, Word, and text documents.
- Index and retrieve answers efficiently from multiple sources.

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• Connect Knowledge Sources:
  Examples:

  • SharePoint libraries (Contracts, Policies)

  • OneDrive folders

  • Teams document repositories

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Step 5: Test and Deploy 🛠️

• Test the agent in Preview Mode.

• Verify:

  • It uses only allowed sources.

  • It returns correct and concise answers.

• Publish and share with your organization.

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✅ 3. Visual Flow Diagram

Below is a representation of the Copilot Agent creation process:

✅ 4. Example Copilot Configuration

Description:
"Document Search Agent – Reads and indexes organizational documents to provide accurate answers based on their content."

Instructions:

You are a Copilot agent that answers questions only using provided documents.
If the answer is not present, say: "The information is not available in the provided documents."
Always provide concise responses and mention the source document name where possible.
Do not use external information.

Knowledge Sources:

• SharePoint Site: Company Contracts

• OneDrive Folder: HR Policies

• Teams Channel Files: Project Documentation

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✅ Best Practices

• Write explicit guardrails to prevent hallucination.

• Ensure data access is restricted to approved sources.

• Monitor agent responses and refine as needed.

Reference: Copilot Developer Camp

🚀 Deploying a Python Flask Application on Windows Server 2022 Datacenter

 

Deploying a Python Flask Application on Windows Server 2022 Datacenter

Deploying a Python Flask application on a Windows Server 2022 Datacenter environment can be a powerful way to host scalable web services within enterprise infrastructure. While Flask is lightweight and flexible, Windows Server offers robust security, centralized management, and compatibility with enterprise tools. However, successful deployment requires careful planning and execution.

Why Flask on Windows Server?

Flask is a micro web framework for Python that allows developers to build web applications quickly and with minimal overhead. Windows Server 2022 Datacenter, on the other hand, is designed for high-performance workloads, virtualization, and hybrid cloud integration. Combining the two can be ideal for internal tools, dashboards, APIs, or even customer-facing applications.

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Key Deployment Considerations

Before deploying, it's essential to understand the components involved:

• Python Environment: Ensure Python (e.g., version 3.11) is installed and configured properly.
• Web Server: Choose between IIS (Internet Information Services) with FastCGI or a standalone WSGI server like Waitress.
• Database: Configure and secure access to the database (e.g., PostgreSQL, MySQL, or SQLite).
• Security: Apply SSL certificates, configure firewalls, and set up user permissions.
• Monitoring & Logging: Implement logging mechanisms and performance monitoring tools.

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Deployment Checklist

A deployment checklist helps streamline the process and avoid common pitfalls. Here's a breakdown:

✅ Pre-Deployment

• Verify server access and administrative privileges.
• Install Python and set up a virtual environment.
• Open necessary ports (e.g., 80 for HTTP, 443 for HTTPS).
• Install required Python packages using requirements.txt.
• Prepare the database and test connectivity.
• Ensure SSL certificates are available if HTTPS is needed.

🚀 Deployment

• Upload application files to the server.
• Activate the virtual environment and install dependencies.
• Configure environment variables and secrets.
• Set up the web server to serve the Flask app.
• Apply SSL and configure reverse proxy if needed.
• Test endpoints and application behavior.

🔄 Post-Deployment

• Monitor logs for errors and performance issues.
• Validate database operations and backups.
• Set up alerts and uptime monitoring.
• Document the deployment process for future reference.
• Notify stakeholders of successful deployment.

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Conclusion

Deploying a Flask application on Windows Server 2022 Datacenter is a strategic choice for organizations seeking performance, control, and integration with existing infrastructure. By following a structured deployment checklist, teams can ensure a smooth rollout, minimize downtime, and maintain a secure and scalable environment.

🧠 Steps to Build a POC Locally with Windows AI Foundry

 🚀 Introduction

What is Foundry Local?

Foundry Local brings the power and trust of Azure AI Foundry to your device. It includes everything you need to run AI apps locally.

As AI adoption grows, developers are increasingly looking for ways to build intelligent applications that run efficiently on local machines. Windows AI Foundry, especially its Foundry Local feature, enables developers to create AI-powered Proof of Concepts (POCs) without relying on cloud infrastructure or Azure subscriptions. This article walks you through the step-by-step process of building a POC locally using Windows AI Foundry—and addresses common challenges along the way.

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🧰 Prerequisites

Before you begin, ensure you have:

• A Windows 11 machine with sufficient CPU/GPU/NPU resources.
• Internet access (for initial setup and model downloads).
• Familiarity with command-line tools and programming (Python, C#, or JavaScript).
• Installed tools:
  • Foundry CLI
  • Foundry Local SDK
  • ONNX Runtime (optional)

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🛠️ Step-by-Step Guide

Step 1: Install Foundry CLI and SDK

Install the Foundry CLI:

winget install FoundryCLI

Install the SDK for your preferred language (e.g., Python):

pip install foundry-sdk

Step 2: Choose and Download a Model

Foundry Local supports several optimized models:

• Phi-4 Reasoning
• Mistral
• Qwen 2.5 Instruct
• DeepSeek R1

Download a model:

foundry models download phi-4

Step 3: Run Inference Locally

Run inference directly from the CLI:

foundry model run phi-4 --input "Explain quantum computing in simple terms."

Step 4: Build Your Application

Example using Python:

from foundry import FoundryModel

model = FoundryModel("phi-4")

response = model.run("What is the capital of Karnataka?")

print(response)

Step 5: Test and Iterate

Test your application with different inputs. Monitor performance and refine prompts or model selection as needed.

⚠️ Common Challenges and Solutions

If winget is blocked by organization policies, try this:

🔁 Manual Installation

1. Visit the official Foundry Local page:
   Windows AI Foundry Dev Blog
2. Download the Foundry Local Installer (MSI or EXE).
3. Run the installer as Administrator.
4. Follow the prompts to complete installation.

🧪 Verify Installation

Open Command Prompt and run:

foundry --version

🤖 List Available Models

foundry model list

This will show models like:

• phi-3.5-mini
• phi-4
• mistral-7b
• qwen-2.5
• deepseek-r1

Challenge	Description	Solution
Hardware Limitations	Some models require significant memory or GPU/NPU support.	Use lightweight models like Phi-4 or quantized ONNX versions.
Model Compatibility	Not all models are optimized for local inference.	Stick to models officially supported by Foundry Local or convert models to ONNX format.
Latency Issues	Inference may be slow on older machines.	Use smaller models or optimize with ONNX Runtime and hardware acceleration.
Limited Documentation	Foundry Local is relatively new, so community support is still growing.	Refer to the official Foundry blog and GitHub issues for guidance.
Integration Complexity	Integrating AI into existing apps can be tricky.	Use SDKs and sample code provided by Microsoft to speed up development.

📈 Use Cases for Local POCs

• Customer Support Bots
• Offline Educational Tools
• Secure Enterprise Assistants
• Healthcare Decision Support (with local data)

To run Windows AI Foundry (Foundry Local) effectively on your Tata Communications machine, here are the minimum and recommended hardware requirements for CPU, GPU, and NPU:

✅ Minimum Requirements

These are sufficient for basic model inference (e.g., small models like Phi-3.5-mini):

• Operating System: Windows 10 (x64), Windows 11 (x64/ARM), or Windows Server 2025
• CPU: Any modern x64 processor (Intel i5/Ryzen 5 or better)
• RAM: 8 GB
• Disk Space: 3 GB free
• Acceleration (Optional): None required—CPU-only inference is supported 
  1

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🌟 Recommended Requirements

For smoother performance and support for larger models:

• CPU: Intel i7/Ryzen 7 or better
• RAM: 16 GB or more
• Disk Space: 15–20 GB free (for model caching)
• GPU:
  • NVIDIA: RTX 2000 series or newer
  • AMD: Radeon 6000 series or newer
• NPU:
  • Qualcomm Snapdragon X Elite (with 8 GB or more VRAM)
  • Apple Silicon (for macOS users)

Foundry Local automatically detects your hardware and downloads the most optimized model variant (CPU, GPU, or NPU) accordingly

🧩 Conclusion

Windows AI Foundry’s local capabilities make it easier than ever to build powerful, privacy-preserving AI applications without cloud dependencies. By understanding the setup process and proactively addressing common challenges, developers can rapidly prototype and deploy intelligent solutions on Windows devices.

https://github.com/microsoft/Foundry-Local/blob/main/docs/README.md

https://learn.microsoft.com/en-us/azure/ai-foundry/foundry-local/get-started

https://learn.microsoft.com/en-us/azure/ai-foundry/foundry-local/reference/reference-cli

Link: Get started with Azure AI Foundry

https://learn.microsoft.com/en-us/azure/ai-foundry/quickstarts/get-started-code?tabs=azure-ai-foundry&pivots=fdp-project

🧠 Sample Use Cases for Foundry Local + Phi-4

1. Offline Customer Support Assistant

• Scenario: A local chatbot that helps employees or customers with FAQs.
• Why Local?: No internet dependency; ideal for secure environments.
• Example Prompt: “How do I reset my company email password?”

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2. Internal Knowledge Search Tool

• Scenario: Search and summarize internal documents or policies.
• Why Local?: Keeps sensitive data on-device.
• Example Prompt: “Summarize the leave policy from this PDF.”

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3. Educational Tutor App

• Scenario: A desktop app that helps students learn topics interactively.
• Why Local?: Works in low-connectivity areas like rural schools.
• Example Prompt: “Explain Newton’s laws with examples.”

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4. Healthcare Assistant (Private Clinics)

• Scenario: Helps doctors or staff with medical terminology or patient instructions.
• Why Local?: Ensures patient data privacy.
• Example Prompt: “What are the symptoms of dengue?”

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5. Coding Helper for Developers

• Scenario: Local assistant that helps write or debug code.
• Why Local?: No need to send code snippets to the cloud.
• Example Prompt: “Write a Python function to sort a list of dictionaries by age.”

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6. Legal Document Analyzer

• Scenario: Summarizes or explains legal clauses from contracts.
• Why Local?: Keeps sensitive legal data secure.
• Example Prompt: “Summarize clause 4.2 of this agreement.”

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7. Multilingual Translator

• Scenario: Translate local language documents or messages.
• Why Local?: Works offline and avoids sending data to external servers.
• Example Prompt: “Translate this Kannada sentence to English.”

Monitoring CrowdStrike Falcon Sensor on Linux and Windows Servers

 CrowdStrike Falcon is a leading endpoint protection solution used across enterprise environments to detect and respond to threats in real time. Ensuring that the Falcon Sensor is active and functioning on both Linux and Windows servers is essential for maintaining a secure infrastructure.

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Why Sensor Monitoring Matters

The Falcon Sensor is the agent that communicates with CrowdStrike’s cloud-based platform. If the sensor is inactive, misconfigured, or not installed, the server may be exposed to threats and lack visibility in the CrowdStrike dashboard.

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Checking Sensor Status on Linux Servers

Method 1: Using Falcon Control Tool

CrowdStrike provides a command-line utility called falconctl:

sudo /opt/CrowdStrike/falconctl -g --status

his command displays the sensor’s configuration and operational status, including registration and connectivity.

Method 2: Using systemd

sudo systemctl status falcon-sensor

Expected output:

● falcon-sensor.service - CrowdStrike Falcon Sensor
   Loaded: loaded (/etc/systemd/system/falcon-sensor.service; enabled)
   Active: active (running) since ...

To start or enable the service:

sudo systemctl start falcon-sensor

sudo systemctl enable falcon-sensor

Checking Sensor Status on Windows Servers

Method 1: Using PowerShell

Run the following command:

Get-Service -Name CSFalconSensor

Expected output:

Status   Name               DisplayName
------   ----               -----------
Running  CSFalconSensor     CrowdStrike Falcon Sensor

Method 2: Listing All Services

If the above command throws an error, list all services and search manually:

Get-Service

Look for:

Running  CSFalconSensor     CrowdStrike Falcon Sensor

If the service is not running, you can start it with:

Start-Service -Name CSFalconSensor

Troubleshooting Tips

• Sensor Not Found: Ensure the sensor is installed in the correct directory (/opt/CrowdStrike/ on Linux or listed in services on Windows).
• Service Fails to Start: Check logs (journalctl -u falcon-sensor on Linux or Event Viewer on Windows).
• Connectivity Issues: Use falconctl or check CrowdStrike dashboard for registration status.

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Conclusion

Regular monitoring of the CrowdStrike Falcon Sensor across both Linux and Windows servers is a vital part of maintaining endpoint security. These simple checks help ensure that your systems are protected and visible in the CrowdStrike console.

🗂️ Migrating Files from SharePoint Online to OneDrive: Methods, Challenges & Solutions

Migrating files from SharePoint Online to OneDrive for Business is a common task for IT teams aiming to streamline collaboration, reorganize data, or decommission legacy sites. While the process can be straightforward with the right tools, it also presents several challenges that require careful planning and execution.

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🚚 Migration Methods

1. SharePoint Migration Tool (SPMT)

• Best for: Structured, bulk migrations.
• Pros: Retains metadata, supports folder/site-level migration, provides logs.
• Cons: Requires setup and may need scripting for complex scenarios.

2. OneDrive Sync App

• Best for: Small-scale or manual migrations.
• Pros: Easy to use, no special setup.
• Cons: Not scalable, no metadata retention.

3. PowerShell Automation

• Best for: Custom, automated migrations.
• Pros: Highly flexible, supports scripting and scheduling.
• Cons: Requires technical expertise.

4. Manual Download & Upload

• Best for: Very small migrations.
• Pros: Simple and direct.
• Cons: Time-consuming, risk of data loss.

5. Third-Party Tools

• Best for: Enterprise-grade migrations.
• Pros: Advanced features, granular control, support for hybrid environments.
• Cons: Licensing costs, learning curve.

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⚠️ Common Challenges & ✅ Solutions

🔄 Challenge 1: Volume of Data (e.g., 1TB+)

• Problem: Large migrations can be slow and error prone.
• Solution: Use SPMT or third-party tools with throttling and retry mechanisms. Break migration into department-wise batches.

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🧩 Challenge 2: Folder Structure Preservation

• Problem: Losing hierarchy during migration can confuse users.
• Solution: Pre-create folder structures in OneDrive using PowerShell or manually. Map SharePoint folders to OneDrive destinations in SPMT.

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🔐 Challenge 3: Permissions and Access

• Problem: OneDrive is personal; SharePoint is collaborative.
• Solution: Use shared folders or Microsoft 365 Groups. Post-migration, assign access manually or via scripts.

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📄 Challenge 4: Metadata Loss

• Problem: Manual methods don’t retain metadata (created by, modified date, etc.).
• Solution: Use SPMT or third-party tools that preserve metadata during migration.

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🧪 Challenge 5: File Integrity Verification

• Problem: Files may be corrupted or incomplete.
• Solution: Use SPMT logs, checksum tools, or spot-check files. Maintain a migration checklist.

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👥 Challenge 6: User Confusion Post-Migration

• Problem: Users may not know where files are or how to share them.
• Solution: Conduct training sessions, provide documentation, and set up support channels.

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🗑️ Challenge 7: Decommissioning Old SharePoint Sites

• Problem: Risk of deleting active or needed content.
• Solution: Archive sites before deletion. Confirm with stakeholders and maintain backups.

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✅ Post-Migration Checklist

• 🔍 Verify file integrity and completeness.
• 🔐 Review and assign permissions.
• 📘 Train users on OneDrive usage and sharing.
• 🗑️ Archive or decommission old SharePoint sites.

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📌 Final Thoughts

Migrating from SharePoint to OneDrive is more than just moving files—it’s about ensuring continuity, usability, and security. By understanding the challenges and applying the right solutions, organizations can achieve a smooth transition that empowers users and improves collaboration.