To optimize our AgenticOps workflow, we need a structured grading system that evaluates each agent’s output. These scores will drive continuous improvement, refining both workflow logic and AI models.

1️⃣ First Principles: Why Grade Agent Outputs?

1. Measure Effectiveness – Quantify the performance of automated actions.
2. Improve Decision-Making – Identify patterns in approved vs. rejected outputs.
3. Fine-Tune AI Agents – Adjust response generation models based on feedback.
4. Reduce Human Intervention – Increase automation where confidence is high.

2️⃣ Agent Performance Grading System

Each agent’s output can be graded based on predefined evaluation criteria.

2.1 Defining the Grading Criteria

For each AgenticOps step, we define a scoring model (0-100) based on key metrics:

Example:

• If an AI-generated reply is rejected, log why (e.g., “Too formal,” “Missing details”).
• If a categorization error occurs, adjust classification model weights.

3️⃣ Implementation in Power Automate

Step 1: Store Grading Data

• Each agent’s output is scored after human review.
• Store feedback in database, Azure Blob, Dataverse, SharePoint, or SQL DB.

Step 2: Automate Feedback Processing

• If an agent scores below a threshold, flag for model retraining.
• If an agent performs well consistently, increase automation confidence.

Step 3: Adjust AI Models Dynamically

• Use Azure OpenAI fine-tuning for response agents.
• Use reinforcement learning for decision-making agents.
• Optimize categorization AI models with feedback.

Step 4: Power BI Dashboard for Analytics

• Track agent performance over time.
• Identify patterns in rejections and bottlenecks.
• Provide insights for workflow tuning.

4️⃣ Adaptive Learning & Continuous Improvement

How The System Evolves

1. Each agent’s performance is logged.
2. Feedback is analyzed in real-time.
3. Underperforming models are flagged for updates.
4. Over time, AI agents improve their accuracy.
5. Manual review workload decreases as automation confidence grows.

Scaling This System

• Introduce self-adjusting automation thresholds based on past performance.
• Train AI to predict when human review is necessary.
• Implement continuous learning pipelines for AI model updates.

5️⃣ What’s Next?

• Where should we log agent grades? (database, Azure Blob, Dataverse, or SharePoint?)
• How frequently should we retrain AI models? (Weekly, Monthly?)
• Do you want Power BI dashboards to track agent performance trends?

This graded feedback system will ensure that AgenticOps evolves into a highly optimized, self-improving workflow. I’ll grade your agents if you grade mine! 🚀