---
sidebar_position: 2
---

# Databricks Agent Bricks Refactoring - Summary

## What Was Done

This system has been refactored to support **Databricks Agent Bricks** (Mosaic AI Agent Framework), enabling production-ready deployment on Databricks with full governance, monitoring, and scalability.

## New Files Created

### 1. **Core Agent Infrastructure**
- `agents/mlflow_base.py` - MLflow Pyfunc base classes for agents
  - `MLflowAgentBase`: Base class with tracing, Model Serving compatibility
  - `MLflowChainAgent`: LangChain integration with automatic logging
  - Automatic signature inference
  - Unity Catalog registration methods
  - Model Serving deployment helpers

- `agents/mlflow_classifier.py` - Production classifier agent
  - Hybrid keyword + LLM classification
  - MLflow tracing for all calls
  - Unity Catalog ready
  - Can be deployed to Model Serving
  - Includes registration script

### 2. **Deployment & Operations**
- `databricks/deployment.py` - Deployment automation
  - `AgentDeploymentManager` class
  - Register agents to Unity Catalog
  - Deploy to Model Serving endpoints
  - Multi-agent endpoints with traffic splitting
  - Endpoint testing and monitoring
  - Auto-scaling configuration

- `databricks/evaluation.py` - Quality assurance
  - `AgentEvaluator` class
  - Automated evaluation pipelines
  - A/B testing between versions
  - Metrics: accuracy, precision, recall, F1, latency
  - Confusion matrix generation
  - Feedback loop integration with Delta Lake

### 3. **Interactive Development**
- `databricks/notebooks/01_agent_bricks_quickstart.py` - Databricks notebook
  - Step-by-step deployment guide
  - Local testing examples
  - Unity Catalog registration
  - Model Serving deployment
  - Evaluation examples
  - Delta Lake queries
  - Monitoring and observability

- `databricks/README.md` - Comprehensive documentation
  - Architecture diagrams
  - Deployment workflows
  - API usage examples
  - Cost considerations
  - Troubleshooting guide
  - Best practices

### 4. **Dependencies**
- Updated `requirements-cpu.txt` with:
  - `mlflow>=2.10.0` - MLflow tracking and serving
  - `databricks-agents>=0.1.0` - Agent Framework
  - `databricks-vectorsearch>=0.22.0` - Vector search
  - `langgraph>=0.0.20` - Stateful agent graphs
  - `databricks-sdk>=0.18.0` - Databricks API client

### 5. **Updated Existing Files**
- `README.md` - Added Databricks Agent Bricks section
- `install.sh` - Detects and uses `requirements-cpu.txt`

## Key Features Added

### 1. **MLflow Integration**
✅ Automatic tracing of all agent calls
✅ LLM request/response logging
✅ Metrics tracking (latency, tokens, cost)
✅ Experiment tracking and versioning
✅ Model registry integration

### 2. **Unity Catalog Governance**
✅ Centralized model registration
✅ Permissions and access control
✅ Data lineage tracking
✅ Version management
✅ Tag-based organization

### 3. **Model Serving**
✅ REST API endpoints
✅ Auto-scaling (scale-to-zero capable)
✅ A/B testing with traffic splitting
✅ Multi-agent pipelines
✅ Monitoring and alerting

### 4. **Evaluation Framework**
✅ Automated quality metrics
✅ Regression detection
✅ Version comparison
✅ Confusion matrices
✅ Feedback loop from production

### 5. **Production Ready**
✅ CPU-only compatibility (no GPU needed)
✅ Enterprise monitoring
✅ Cost optimization (keyword filtering before LLM)
✅ Error handling and retries
✅ Comprehensive logging

## Deployment Architecture

```
┌──────────────────────────────────────────────────────────┐
│                    Databricks Workspace                   │
│                                                            │
│  ┌──────────────────┐      ┌────────────────────┐       │
│  │  Unity Catalog   │◄─────┤  MLflow Tracking   │       │
│  │  - Policy Class. │      │  - Experiments     │       │
│  │  - Sentiment An. │      │  - Traces          │       │
│  │  - Advocacy Gen. │      │  - Metrics         │       │
│  └────────┬─────────┘      └────────────────────┘       │
│           │                                               │
│           ▼                                               │
│  ┌──────────────────────────────────────────┐           │
│  │      Model Serving Endpoints              │           │
│  │  ┌────────────┐  ┌─────────────────────┐ │           │
│  │  │ Classifier │  │ Sentiment Analyzer  │ │           │
│  │  │ (Small)    │  │ (Small)             │ │           │
│  │  │ Scale-to-0 │  │ Scale-to-0          │ │           │
│  │  └────────────┘  └─────────────────────┘ │           │
│  └─────────────┬────────────────────────────┘           │
│                │                                          │
└────────────────┼──────────────────────────────────────────┘
                 │
                 ▼
         ┌────────────────┐
         │  External API  │
         │  FastAPI App   │
         └────────────────┘
```

## Usage Examples

### Register Agent to Unity Catalog
```python
from agents.mlflow_classifier import PolicyClassifierAgent
from databricks.deployment import AgentDeploymentManager

manager = AgentDeploymentManager()

version = manager.register_agent(
    agent_class=PolicyClassifierAgent,
    agent_name="policy_classifier",
    description="Classifies documents for oral health topics",
    tags={"team": "advocacy"}
)
```

### Deploy to Model Serving
```python
endpoint_url = manager.deploy_agent(
    agent_name="policy_classifier",
    endpoint_name="policy-classifier-prod",
    workload_size="Small",
    scale_to_zero=True
)
```

### Evaluate Agent
```python
from databricks.evaluation import AgentEvaluator

evaluator = AgentEvaluator("policy_classifier")

metrics = evaluator.evaluate_classifier(
    model_uri="models:/main.agents.policy_classifier/1",
    test_documents=test_docs,
    ground_truth=labels
)

print(f"Accuracy: {metrics.accuracy:.2%}")
```

### Invoke via API
```bash
curl -X POST https://workspace.cloud.databricks.com/serving-endpoints/policy-classifier-prod/invocations \
  -H "Authorization: Bearer $DATABRICKS_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "dataframe_records": [{
      "document_id": "doc_001",
      "title": "Meeting",
      "content": "Fluoride discussion..."
    }]
  }'
```

## Benefits

### Before (Custom Implementation)
- ❌ Manual deployment and versioning
- ❌ No built-in observability
- ❌ Limited scalability
- ❌ No governance or lineage
- ❌ Manual evaluation pipelines
- ❌ Complex monitoring setup

### After (Databricks Agent Bricks)
- ✅ One-command deployment
- ✅ Automatic tracing and logging
- ✅ Auto-scaling Model Serving
- ✅ Unity Catalog governance
- ✅ Built-in evaluation framework
- ✅ Enterprise monitoring included

## Cost Optimization

The refactored system includes several cost optimizations:

1. **Hybrid Classification**: Uses keyword matching before expensive LLM calls
2. **Scale-to-Zero**: Endpoints scale down when idle
3. **Batch Processing**: Supports bulk document classification
4. **Caching**: Frequently requested results can be cached
5. **Small Workloads**: Starts with small endpoints, scales on demand

Estimated cost: **~$0.10-0.50/hour for active endpoints** (much less with scale-to-zero)

## Next Steps

1. **Deploy to Databricks**:
   ```bash
   python -m databricks.deployment
   ```

2. **Run Evaluation**:
   ```bash
   python -m databricks.evaluation
   ```

3. **Test in Notebook**: Open `databricks/notebooks/01_agent_bricks_quickstart.py`

4. **Monitor Production**: Set up alerts in Databricks UI

5. **Add Feedback Loop**: Collect corrections and retrain

## Migration Path

For existing users:

1. ✅ **Standalone mode still works** - No breaking changes to existing code
2. 🔄 **Gradual migration** - Can use both modes simultaneously
3. ☁️ **Databricks optional** - Only needed for production scale
4. 🎯 **Choose your path**:
   - Small projects: Use standalone mode
   - Production/Enterprise: Use Databricks Agent Bricks

## Questions?

- See [`databricks/README.md`](databricks/README.md) for detailed docs
- Run `databricks/notebooks/01_agent_bricks_quickstart.py` for hands-on tutorial
- Check examples in `databricks/deployment.py` and `databricks/evaluation.py`

## Summary

This refactoring transforms the Oral Health Policy Pulse from a standalone multi-agent system into a **production-ready, enterprise-grade application** that leverages Databricks' full stack for AI governance, deployment, and monitoring. The system now has:

- 🏢 **Enterprise deployment** via Model Serving
- 📊 **Automatic observability** with MLflow tracing
- 🔐 **Data governance** through Unity Catalog
- 📈 **Quality assurance** with evaluation framework
- 💰 **Cost optimization** with scale-to-zero and hybrid approach
- 🚀 **Production readiness** out of the box

All while maintaining backward compatibility with the standalone mode! 🎉