# Google AI Plugins Audit Report

**Created:** November 13, 2025
**Purpose:** Comprehensive audit of all jeremy-* plugins related to Google Vertex AI, Gemini, and Firebase
**Scope:** Observability, telemetry, storage integration, and feature completeness

---

## Executive Summary

**Total Plugins Audited:** 11 Google AI-related plugins
**Critical Gaps Identified:**
- ⚠️ 10/11 plugins missing **observability and telemetry** documentation
- ⚠️ 11/11 plugins missing **Vertex AI storage integration** details
- ⚠️ 2/11 plugins **missing README files** entirely

**New Google Features (2025) Not Yet Documented:**
1. **Agent Engine Observability Dashboard** (token usage, latency, error rates)
2. **Cloud Trace Integration** with OpenTelemetry support
3. **Evaluation Layer** for simulating user interactions
4. **BigQuery Connectors** for periodic data sync
5. **Cloud Storage Integration** with incremental refresh

---

## Plugin Inventory

### AI/ML Category (5 plugins)

| Plugin | README | Lines | Observability | Storage | Status |
|--------|--------|-------|---------------|---------|--------|
| **jeremy-adk-orchestrator** | ❌ Missing | 0 | ❌ | ❌ | 🔴 Critical |
| **jeremy-gcp-starter-examples** | ✅ Yes | 295 | ⚠️ Minimal (1 mention) | ❌ | 🟡 Needs Update |
| **jeremy-genkit-pro** | ✅ Yes | 204 | ❌ | ❌ | 🟡 Needs Update |
| **jeremy-vertex-engine** | ✅ Yes | 195 | ❌ | ❌ | 🟡 Needs Update |
| **jeremy-vertex-validator** | ✅ Yes | 32 | ❌ | ❌ | 🟡 Needs Update |

### Community Category (2 plugins)

| Plugin | README | Lines | Observability | Storage | Status |
|--------|--------|-------|---------------|---------|--------|
| **jeremy-firebase** | ❌ Missing | 0 | ❌ | ❌ | 🔴 Critical |
| **jeremy-firestore** | ✅ Yes | 615 | ❌ | ❌ | 🟡 Needs Update |

### DevOps Category (4 plugins)

| Plugin | README | Lines | Observability | Storage | Status |
|--------|--------|-------|---------------|---------|--------|
| **jeremy-adk-terraform** | ✅ Yes | 34 | ❌ | ❌ | 🟡 Needs Update |
| **jeremy-genkit-terraform** | ✅ Yes | 34 | ❌ | ❌ | 🟡 Needs Update |
| **jeremy-github-actions-gcp** | ✅ Yes | 410 | ❌ | ❌ | 🟡 Needs Update |
| **jeremy-vertex-terraform** | ✅ Yes | 34 | ❌ | ❌ | 🟡 Needs Update |

---

## Detailed Plugin Analysis

### 🔴 CRITICAL: Missing README Files

#### jeremy-adk-orchestrator
**Location:** `plugins/ai-ml/jeremy-adk-orchestrator/`
**Status:** Has plugin.json but **NO README**

**Required Content:**
- What is ADK Orchestrator and why use it
- Supervisory orchestration patterns
- A2A protocol management
- Multi-agent system examples
- Memory Bank integration
- **NEW:** Observability dashboard configuration
- **NEW:** Cloud Trace integration for distributed tracing
- **NEW:** BigQuery connector setup for data pipelines

**Priority:** 🔴 **CRITICAL** - Must create comprehensive README

---

#### jeremy-firebase
**Location:** `plugins/community/jeremy-firebase/`
**Status:** Has plugin.json but **NO README**
**Note:** Has extensive user manuals (001-008) in `000-usermanuals/` directory

**Required Content:**
- Firebase platform operations overview
- Vertex AI Gemini integration
- Cloud Functions deployment
- Firestore operations
- Authentication management
- **NEW:** Cloud Storage integration patterns
- **NEW:** BigQuery export/import for Firebase data
- **NEW:** Cloud Logging and Monitoring setup
- **NEW:** Performance Monitoring integration

**Priority:** 🔴 **CRITICAL** - Must create README (even though manuals exist)

---

### 🟡 NEEDS UPDATE: Existing Plugins Missing New Features

#### jeremy-vertex-engine
**Location:** `plugins/ai-ml/jeremy-vertex-engine/`
**Current README:** 195 lines
**Purpose:** Agent Engine inspection and orchestration

**Missing Features:**
1. **Observability Dashboard** (November 2025 release)
   - Token usage tracking
   - Latency metrics
   - Error rate monitoring
   - Tool call analytics

2. **Cloud Trace Integration**
   - OpenTelemetry support
   - Distributed tracing for multi-agent systems
   - Span visualization in Cloud Console

3. **Cloud Monitoring Integration**
   - Custom metrics for agent performance
   - Alerting policies for agent health
   - SLI/SLO configuration

4. **Storage Integration**
   - BigQuery connectors for agent logs
   - Cloud Storage for agent artifacts
   - Session data persistence options

**Recommended Additions:**
```markdown
## Observability & Monitoring

### Agent Engine Observability Dashboard
Track key metrics for deployed agents:
- **Token Usage:** Monitor tokens consumed per agent/session
- **Latency:** Measure first-token and end-to-end latency
- **Error Rates:** Track API errors and failure patterns
- **Tool Calls:** Analyze tool usage and performance

### Cloud Trace Integration
Enable distributed tracing with OpenTelemetry:
\`\`\`python
from google.cloud import trace_v1

tracer = trace_v1.TraceServiceClient()
# Automatic trace instrumentation for agents
\`\`\`

### Cloud Logging
Query agent logs for debugging:
\`\`\`bash
gcloud logging read "resource.type=vertex_ai_agent_engine AND
  resource.labels.agent_id=AGENT_ID" --limit 50
\`\`\`

## Storage Integration

### BigQuery Connector
Export agent interactions to BigQuery for analytics:
\`\`\`python
from google.cloud import aiplatform

client.agent_engines.create_bigquery_connector(
    agent_engine_id=AGENT_ENGINE_ID,
    dataset="project.agent_logs",
    sync_frequency="HOURLY"
)
\`\`\`
```

---

#### jeremy-genkit-pro
**Location:** `plugins/ai-ml/jeremy-genkit-pro/`
**Current README:** 204 lines
**Purpose:** Firebase Genkit production workflows

**Missing Features:**
1. **Observability for Genkit Flows**
   - Flow execution tracing
   - Step-level performance metrics
   - Error tracking and debugging

2. **Cloud Storage Integration**
   - Artifact storage for flow outputs
   - State persistence across executions
   - Checkpoint management

3. **BigQuery Integration**
   - Flow execution logs export
   - Analytics on flow performance
   - Historical trend analysis

**Recommended Additions:**
```markdown
## Genkit Flow Observability

### Trace Flow Executions
Track each step in your Genkit flows:
\`\`\`typescript
import { defineFlow } from '@genkit-ai/flow';
import { enableTracing } from '@genkit-ai/cloud-trace';

enableTracing({
  projectId: PROJECT_ID,
  serviceName: 'my-genkit-app'
});

export const myFlow = defineFlow(
  { name: 'myFlow', inputSchema: z.string() },
  async (input) => {
    // Automatic tracing for each step
    const result = await llm.generate(input);
    return result;
  }
);
\`\`\`

### Export Logs to BigQuery
\`\`\`typescript
import { configureBigQueryExport } from '@genkit-ai/monitoring';

configureBigQueryExport({
  projectId: PROJECT_ID,
  dataset: 'genkit_logs',
  table: 'flow_executions'
});
\`\`\`

## Cloud Storage Integration

### Save Flow Artifacts
\`\`\`typescript
import { Storage } from '@google-cloud/storage';

const storage = new Storage();
const bucket = storage.bucket('flow-artifacts');

export const myFlow = defineFlow(async (input) => {
  const result = await processData(input);

  // Save to Cloud Storage
  await bucket.file(`output-${Date.now()}.json`).save(
    JSON.stringify(result)
  );

  return result;
});
\`\`\`
```

---

#### jeremy-vertex-validator
**Location:** `plugins/ai-ml/jeremy-vertex-validator/`
**Current README:** 32 lines (very short!)
**Purpose:** Production readiness validation

**Missing Features:**
1. **Observability Validation Checks**
   - Verify Cloud Trace is configured
   - Check Cloud Logging integration
   - Validate monitoring dashboards exist
   - Test alerting policies

2. **Storage Integration Validation**
   - Verify BigQuery connector setup
   - Check Cloud Storage bucket permissions
   - Validate data export configurations

**Recommended Additions:**
```markdown
## Observability Validation

### Check Cloud Trace Configuration
\`\`\`bash
# Verify trace sampling is enabled
gcloud ai agent-engines describe AGENT_ENGINE_ID \
    --format="value(observabilityConfig.traceSamplingRate)"
\`\`\`

### Validate Monitoring Dashboards
\`\`\`python
from google.cloud import monitoring_v3

client = monitoring_v3.DashboardServiceClient()
dashboards = client.list_dashboards(
    request={"parent": f"projects/{PROJECT_ID}"}
)

# Check for agent monitoring dashboard
agent_dashboard = next(
    (d for d in dashboards if 'agent-engine' in d.display_name), None
)

if not agent_dashboard:
    print("⚠️ WARNING: No agent monitoring dashboard found")
\`\`\`

## Storage Integration Validation

### Verify BigQuery Connector
\`\`\`python
from google.cloud import bigquery

client = bigquery.Client()

# Check if agent logs table exists
table_id = f"{PROJECT_ID}.agent_logs.interactions"
try:
    client.get_table(table_id)
    print("✅ BigQuery table exists")
except Exception:
    print("❌ ERROR: BigQuery table not found")
\`\`\`
```

---

#### jeremy-gcp-starter-examples
**Location:** `plugins/ai-ml/jeremy-gcp-starter-examples/`
**Current README:** 295 lines
**Observability Mentions:** 1 (minimal)

**Missing Features:**
1. **Observability Examples**
   - Example dashboard configurations
   - Sample Cloud Trace queries
   - Monitoring alert examples

2. **Storage Integration Examples**
   - BigQuery export examples
   - Cloud Storage patterns
   - Data pipeline templates

**Recommended Additions:**
- Add "observability" section with code examples
- Include BigQuery connector setup examples
- Provide Cloud Trace configuration samples

---

#### jeremy-firestore
**Location:** `plugins/community/jeremy-firestore/`
**Current README:** 615 lines (most comprehensive)

**Missing Features:**
1. **Firestore → BigQuery Export**
   - Automatic data export for analytics
   - Incremental sync configuration
   - Query examples for exported data

2. **Cloud Monitoring Integration**
   - Firestore performance metrics
   - Read/write operation monitoring
   - Query performance tracking

**Recommended Additions:**
```markdown
## BigQuery Export Integration

### Enable Firestore BigQuery Export
\`\`\`bash
gcloud firestore export gs://BUCKET_NAME \
    --collection-ids=users,orders \
    --project=PROJECT_ID

# Automated daily export to BigQuery
gcloud scheduler jobs create app-engine firestore-export \
    --schedule="0 2 * * *" \
    --time-zone="America/New_York" \
    --uri="/export-firestore"
\`\`\`

### Query Exported Data in BigQuery
\`\`\`sql
-- Analyze user activity patterns
SELECT
  DATE(TIMESTAMP_MICROS(data.timestamp)) as date,
  COUNT(*) as events
FROM `PROJECT_ID.firestore_export.users`
GROUP BY date
ORDER BY date DESC
LIMIT 30;
\`\`\`

## Firestore Monitoring

### Track Performance Metrics
\`\`\`python
from google.cloud import monitoring_v3

# Monitor Firestore read operations
metric = monitoring_v3.types.TimeSeries()
metric.metric.type = "firestore.googleapis.com/document/read_count"
metric.resource.type = "firestore_instance"

# Create custom dashboard for Firestore metrics
\`\`\`
```

---

#### Terraform Plugins (4 plugins)
**Plugins:** jeremy-adk-terraform, jeremy-genkit-terraform, jeremy-vertex-terraform, jeremy-github-actions-gcp
**Average README Size:** 34-410 lines

**Missing Features (All):**
1. **Observability Terraform Resources**
   - `google_monitoring_dashboard` for agent metrics
   - `google_logging_metric` for custom metrics
   - `google_monitoring_alert_policy` for alerting

2. **Storage Integration Resources**
   - `google_bigquery_data_transfer_config` for scheduled exports
   - `google_storage_bucket` for agent artifacts
   - BigQuery connector Terraform modules

**Recommended Additions:**
```hcl
# Observability Resources

resource "google_monitoring_dashboard" "agent_dashboard" {
  dashboard_json = jsonencode({
    displayName = "Agent Engine Observability"
    mosaicLayout = {
      columns = 12
      tiles = [
        {
          width  = 6
          height = 4
          widget = {
            title = "Token Usage"
            xyChart = {
              dataSets = [{
                timeSeriesQuery = {
                  timeSeriesFilter = {
                    filter = "resource.type=vertex_ai_agent_engine"
                    aggregation = {
                      alignmentPeriod    = "60s"
                      crossSeriesReducer = "REDUCE_SUM"
                      perSeriesAligner   = "ALIGN_RATE"
                    }
                  }
                }
              }]
            }
          }
        }
      ]
    }
  })
}

resource "google_monitoring_alert_policy" "agent_high_error_rate" {
  display_name = "Agent Engine High Error Rate"
  conditions {
    display_name = "Error rate > 5%"
    condition_threshold {
      filter          = "resource.type=vertex_ai_agent_engine AND metric.type=\"agent_engine/error_rate\""
      duration        = "300s"
      comparison      = "COMPARISON_GT"
      threshold_value = 0.05
    }
  }
  notification_channels = [
    google_monitoring_notification_channel.email.id
  ]
}

# Storage Integration Resources

resource "google_bigquery_data_transfer_config" "agent_logs_export" {
  display_name           = "Agent Logs Export"
  location               = var.region
  data_source_id         = "google_cloud_storage"
  schedule               = "every 6 hours"
  destination_dataset_id = google_bigquery_dataset.agent_logs.dataset_id

  params = {
    data_path_template      = "gs://${google_storage_bucket.agent_logs.name}/*.json"
    destination_table_name_template = "agent_interactions"
    file_format                     = "JSON"
  }
}

resource "google_storage_bucket" "agent_artifacts" {
  name          = "${var.project_id}-agent-artifacts"
  location      = var.region
  force_destroy = false

  lifecycle_rule {
    condition {
      age = 90  # Delete artifacts older than 90 days
    }
    action {
      type = "Delete"
    }
  }
}
```

---

## New Google Cloud Features (2025)

### 1. Agent Engine Observability Dashboard

**Released:** November 2025
**Documentation:** https://cloud.google.com/blog/products/ai-machine-learning/more-ways-to-build-and-scale-ai-agents-with-vertex-ai-agent-builder

**Key Features:**
- **Token Usage Tracking:** Monitor tokens consumed per agent, session, and time period
- **Latency Metrics:** Track first-token latency and end-to-end response time
- **Error Rate Monitoring:** Identify failing requests and error patterns
- **Tool Call Analytics:** Analyze which tools are used most frequently

**Access:**
```
Google Cloud Console → Vertex AI → Agent Builder → Agent Engine → [Your Agent] → Observability
```

**Metrics Available:**
- `agent_engine/token_count` - Total tokens processed
- `agent_engine/latency` - Response latency percentiles (p50, p95, p99)
- `agent_engine/error_rate` - Percentage of failed requests
- `agent_engine/tool_calls` - Tool invocation frequency

---

### 2. Cloud Trace Integration with OpenTelemetry

**Feature:** Distributed tracing for agent workflows

**Setup:**
```python
from opentelemetry import trace
from opentelemetry.exporter.cloud_trace import CloudTraceSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor

# Initialize Cloud Trace exporter
trace.set_tracer_provider(TracerProvider())
cloud_trace_exporter = CloudTraceSpanExporter()
trace.get_tracer_provider().add_span_processor(
    BatchSpanProcessor(cloud_trace_exporter)
)

tracer = trace.get_tracer(__name__)

# Automatic tracing for agent calls
with tracer.start_as_current_span("agent_query"):
    response = agent.query("What is the weather?")
```

**View Traces:**
```
Google Cloud Console → Trace → Trace Explorer
Filter by resource.type="vertex_ai_agent_engine"
```

---

### 3. Evaluation Layer for Agent Testing

**Feature:** Simulate user interactions to test agent reliability

**Use Cases:**
- Regression testing before deployments
- A/B testing different agent configurations
- Load testing for capacity planning

**Implementation:**
```python
from google.cloud import aiplatform

# Create evaluation dataset
evaluation_data = [
    {"input": "What's the weather?", "expected_tool": "get_weather"},
    {"input": "Book a flight", "expected_tool": "book_flight"},
]

# Run evaluation
evaluation_result = client.agent_engines.evaluate(
    agent_engine_id=AGENT_ENGINE_ID,
    test_cases=evaluation_data,
    metrics=["accuracy", "latency", "tool_selection"]
)

print(f"Accuracy: {evaluation_result.accuracy}")
print(f"Avg Latency: {evaluation_result.avg_latency_ms}ms")
```

---

### 4. BigQuery Connectors for Periodic Data Sync

**Feature:** Automatic export of agent interactions to BigQuery

**Configuration:**
```python
from google.cloud import discoveryengine_v1

# Create BigQuery connector
connector = discoveryengine_v1.BigQuerySource(
    project_id=PROJECT_ID,
    dataset_id="agent_analytics",
    table_id="interactions",
    data_schema="agent_interaction"
)

# Configure periodic sync
client.data_connectors.create(
    parent=f"projects/{PROJECT_ID}/locations/{LOCATION}/dataStores/{DATA_STORE_ID}",
    data_connector=discoveryengine_v1.DataConnector(
        bigquery_source=connector,
        sync_schedule="0 */6 * * *",  # Every 6 hours
        sync_mode="INCREMENTAL"
    )
)
```

**Query Agent Data:**
```sql
-- Analyze agent performance
SELECT
  DATE(timestamp) as date,
  agent_id,
  COUNT(*) as total_queries,
  AVG(latency_ms) as avg_latency,
  SUM(CASE WHEN error IS NOT NULL THEN 1 ELSE 0 END) as errors
FROM `project.agent_analytics.interactions`
WHERE timestamp >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 7 DAY)
GROUP BY date, agent_id
ORDER BY date DESC;
```

---

### 5. Cloud Storage Integration with Incremental Refresh

**Feature:** Sync data from Cloud Storage to Vertex AI Search with incremental updates

**Setup:**
```python
from google.cloud import discoveryengine_v1

# Create Cloud Storage data store
data_store = client.data_stores.create(
    parent=f"projects/{PROJECT_ID}/locations/global/collections/default_collection",
    data_store=discoveryengine_v1.DataStore(
        display_name="Product Catalog",
        industry_vertical="GENERIC",
        content_config="CONTENT_REQUIRED"
    )
)

# Import from Cloud Storage
import_request = discoveryengine_v1.ImportDocumentsRequest(
    parent=data_store.name,
    gcs_source=discoveryengine_v1.GcsSource(
        input_uris=["gs://my-bucket/products/*.json"],
        data_schema="document"
    ),
    reconciliation_mode="INCREMENTAL"  # Only update changed documents
)

operation = client.documents.import_documents(import_request)
```

---

## Priority Action Items

### 🔴 CRITICAL (Must Complete First)

1. **Create jeremy-adk-orchestrator README** (0 lines → target 300+ lines)
   - Include observability dashboard setup
   - Add Cloud Trace integration examples
   - Document BigQuery connector for multi-agent logs

2. **Create jeremy-firebase README** (0 lines → target 200+ lines)
   - Link to extensive user manuals (001-008)
   - Add observability quick start
   - Include Firebase → BigQuery export guide

### 🟡 HIGH PRIORITY (Complete Within Sprint)

3. **Update jeremy-vertex-engine** (195 lines → target 400+ lines)
   - Add "Observability & Monitoring" section (100 lines)
   - Add "Storage Integration" section (80 lines)
   - Include evaluation layer examples (20 lines)

4. **Update jeremy-genkit-pro** (204 lines → target 350+ lines)
   - Add Genkit flow tracing documentation
   - Include Cloud Storage integration patterns
   - Add BigQuery export configuration

5. **Update jeremy-vertex-validator** (32 lines → target 150+ lines)
   - Add observability validation checks
   - Include storage integration validation
   - Expand with production readiness checklists

### 🟢 MEDIUM PRIORITY (Complete Within Quarter)

6. **Update all Terraform plugins** (4 plugins)
   - Add `google_monitoring_dashboard` resources
   - Add `google_monitoring_alert_policy` resources
   - Add `google_bigquery_data_transfer_config` resources
   - Include example agent observability dashboards

7. **Update jeremy-gcp-starter-examples**
   - Add observability examples section
   - Include BigQuery connector samples
   - Provide Cloud Trace setup examples

8. **Update jeremy-firestore**
   - Add Firestore → BigQuery export section
   - Include Cloud Monitoring integration
   - Document performance tracking

---

## Implementation Checklist

### For Each Plugin Update

- [ ] Add "## Observability & Monitoring" section
- [ ] Document Agent Engine dashboard access
- [ ] Provide Cloud Trace integration examples
- [ ] Include Cloud Logging query examples
- [ ] Add custom metrics configuration

- [ ] Add "## Storage Integration" section
- [ ] Document BigQuery connector setup
- [ ] Provide Cloud Storage integration patterns
- [ ] Include incremental sync configuration
- [ ] Add data export examples

- [ ] Update plugin.json with new keywords
  - Add: "observability", "monitoring", "telemetry"
  - Add: "storage", "bigquery", "cloud-storage"

- [ ] Test all code examples
- [ ] Verify links to official documentation
- [ ] Update plugin version number
- [ ] Create changelog entry

---

## Recommended Documentation Structure

For each plugin, add these sections (if missing):

```markdown
## Observability & Monitoring

### Agent Engine Observability Dashboard
[How to access and use the dashboard]

### Cloud Trace Integration
[OpenTelemetry setup and span visualization]

### Cloud Logging
[Query examples and log analysis]

### Cloud Monitoring
[Custom metrics, dashboards, and alerting]

## Storage Integration

### BigQuery Connectors
[Setup, configuration, and query examples]

### Cloud Storage Integration
[Artifact storage, state persistence, checkpoints]

### Data Export Patterns
[Scheduled exports, incremental sync, data formats]

## Production Best Practices

### Monitoring Setup
[Essential metrics to track]

### Alerting Policies
[Recommended alerts for production]

### Performance Optimization
[Tips for reducing latency and costs]
```

---

## External Resources

### Official Documentation

- **Agent Engine Observability:** https://cloud.google.com/blog/products/ai-machine-learning/more-ways-to-build-and-scale-ai-agents-with-vertex-ai-agent-builder
- **Cloud Trace:** https://cloud.google.com/trace/docs
- **Cloud Logging:** https://cloud.google.com/logging/docs
- **Cloud Monitoring:** https://cloud.google.com/monitoring/docs
- **BigQuery Data Transfer:** https://cloud.google.com/bigquery-transfer/docs
- **Vertex AI Storage Integration:** https://cloud.google.com/vertex-ai/docs/beginner/bqml

### Blog Posts & Announcements

- **InfoWorld (October 2025):** "Google boosts Vertex AI Agent Builder with new observability and deployment tools"
- **Google Cloud Blog:** "More ways to build and scale AI agents with Vertex AI Agent Builder"
- **AI Business:** "Google Intros New Vertex AI Agent Builder Tools"

---

## Audit Completion Metrics

**Documentation Coverage:**
- Observability: 1/11 plugins (9.1%) → Target: 11/11 (100%)
- Storage Integration: 0/11 plugins (0%) → Target: 11/11 (100%)
- README Files: 9/11 plugins (81.8%) → Target: 11/11 (100%)

**Estimated Effort:**
- Critical updates: 40 hours
- High priority updates: 60 hours
- Medium priority updates: 40 hours
- **Total: 140 hours** (3.5 weeks for 1 developer)

---

**Audit Version:** 1.0.0
**Auditor:** Claude Code
**Date:** November 13, 2025
**Status:** Ready for Implementation
**Next Review:** December 13, 2025 (30 days)
