Tagged: power automate

Creating an AgenticOps Powered Email Workflow

Workflow Overview

This is workflow seems simple enough to wrap our heads around. It is complex enough to get a feel for how to build an AgenticOps workflow. You do not need to use an overly complicated platform. Yet, I’m very technical and analytical in my old age. This is easy for me, but it may be harder if you don’t deal with building with technology daily. However, anyone with a little patience and problem-solving ability can handle it.

Here’s the workflow:

Trigger: An email is received (via Outlook connector).

Agent 1: Summarization Agent

  • Extracts key information from the email (e.g., sender intent, action items, important context).
  • Uses Azure OpenAI (GPT/Copilot) or AI Builder for summarization.

Agent 2: Sentiment Analysis Agent

  • Analyzes sentiment (e.g., Positive, Neutral, Negative, Urgent) using:
    • Power Automate AI Builder
    • Azure Cognitive Services Text Analytics
    • GPT-based prompt for sentiment classification
  • Adds a Sentiment Label to guide prioritization.

Agent 3: Categorization Agent

  • Classifies emails into categories such as:
    • Support
    • Sales
    • Urgent
    • Inquiry
    • Spam
  • Uses AI-based classification.

Agent 4: Priority Routing Agent

  • Uses Sentiment + Category to assign a priority level:
    • High Priority (Urgent & Negative Sentiment) → Immediate Action
    • Medium Priority (Neutral Sentiment) → Regular Workflow
    • Low Priority (Positive Sentiment) → Can be delayed

Agent 5: Reply Generation Agent

  • Generates an AI-powered response:
    • Uses Azure OpenAI GPT/Copilot
    • Includes pre-defined templates
    • Formats placeholders (e.g., Client Name, Ticket ID)

Agent 6: Review & Edit Agent

  • Reviews AI-generated response (human or AI).
  • Provides edit suggestions and tracks changes.

Agent 7: Approval Agent

  • Final approval for sending response.
  • Decision options: Approve, Edit, Reject.

Decision Point: Manager (AI or Human)

  • If approved → Send Email
  • If edited → Return for Review
  • If rejected → Escalate for Manual Handling

Action: Send, Revise, or Flag for Manual Review

Implementation in Power Automate

Step 1: Create Power Automate Flow

  • Trigger: New email arrives in Outlook.
  • Filter: Exclude spam using AI-based rules.
  • Extract: Email Body, Sender, Subject for processing.

Step 2: Summarization Agent

  • Use Azure OpenAI GPT, Copilot, or AI Builder for summarization.
  • Return key points from email.

Step 3: Sentiment Analysis Agent

  • Call Azure Cognitive Services – Text Analytics API
  • Classify sentiment: Positive, Neutral, Negative, Urgent
  • Store Sentiment Score & Label

Step 4: Categorization Agent

  • AI-based classification into Support, Sales, Urgent, Inquiry, Spam

Step 5: Priority Routing Agent

  • If Urgent & Negative Sentiment → Escalate Immediately
  • If Positive Sentiment → Queue for Later
  • If Neutral Sentiment → Proceed Normally

Step 6: Reply Generation Agent

  • Generate reply with GPT, Copilot, or AI templates
  • Auto-insert placeholders like [Client Name], [Ticket ID]

Step 7: Review & Edit Agent

  • AI or human suggests modifications to response.
  • Changes are stored in Dataverse or SharePoint.

Step 8: Approval Agent

  • Approve, Edit, or Reject email response.

Step 9: Decision Point (AI Manager or Human)

  • If Approved → Send Email Automatically.
  • If Rejected → Manual Review or Escalation.

Enhancements & Extensions

✅ Logging & Monitoring

  • Track workflow execution, decisions, and feedback.
  • Store logs in Dataverse, SharePoint, or SQL.

✅ Adaptive Workflow

  • Urgent Emails: Send Teams Notification for immediate action.
  • Low-Priority Emails: Add to review queue for later processing.

✅ Integration with Teams

  • Notify Teams channel if approval is required.
  • Allow human managers to approve via Teams.

🚀 Final Questions Before Implementation

  1. Deployment Choice
    • Power Automate Cloud (Fully automated & integrated with Outlook)?
    • Power Automate Desktop (For more local processing)?
  2. Review Process
    • Do you want a human-in-the-loop for reviewing AI responses?
    • Or should this be fully autonomous?
  3. AI Model Preference
    • Azure OpenAI GPT-4/Copilot for Summarization, Categorization & Reply?
    • Azure Cognitive Services for Sentiment Analysis?

Should I write the detailed steps? Need help building this workflow or something like it, let me know, and we can talk it out! 🚀