AI Admin (ai-admin) — Workflow Guide
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This page is scenario-first (operational workflows, real run order, and troubleshooting). For the full autogenerated command/flag catalog, use the CLI Reference.
File Information
| Property | Value |
|---|---|
| Binary Name | ai-admin |
| Version | 9.0.1 |
| File Size | 2.6MB |
| Author | Warith Al Maawali warith@digi77.com |
| License | Proprietary |
| Category | AI & Intelligence |
| Description | AI system administration and maintenance |
| JSON Data | View Raw JSON |
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What ai-admin Does
ai-admin is the maintenance toolbox for the entire AI system. It manages the SQLite database that stores training data, feedback, predictions, and learning history. It provides diagnostics, backup/restore, optimization, and cleanup operations.
Key Capabilities
| Feature | Description |
|---|---|
| Diagnostics | Full system health check for all AI components |
| Database Backup/Restore | Protect AI data with backups and recovery |
| Integrity Checks | Verify database consistency |
| Performance Tuning | Optimize queries, rebuild indexes, clean old data |
| Migration | Update database schema across versions |
Scenario 1: Daily AI Health Check
Quick daily check to ensure the AI system is running correctly.
# Step 1: Run quick diagnostics
ai-admin diagnostics
# Step 2: If issues detected, run full diagnostics
ai-admin diagnostics --full
# Step 3: Check database integrity
ai-admin db integrity-check
# Step 4: View database statistics
ai-admin db info
# Step 5: Analyze recent learning trends
sudo ai-learner analyze
Cross-binary workflow: ai-admin + ai-learner
When to run: Daily, or immediately after noticing ai-cmd accuracy issues.
Scenario 2: Weekly Maintenance Routine
Keep the AI database healthy with regular maintenance.
# Step 1: Create a timestamped backup before any maintenance
ai-admin db backup --output ./backup-$(date +%Y%m%d).db
# Step 2: Check database integrity
ai-admin db integrity-check
# Step 3: Optimize database performance (VACUUM, analyze)
ai-admin tune optimize
# Step 4: Rebuild search indexes for faster queries
ai-admin tune rebuild-index
# Step 5: Clean data older than 30 days
ai-admin tune cleanup --days 30
# Step 6: Verify health after maintenance
ai-admin diagnostics --full
# Step 7: Confirm ai-cmd still works correctly
ai-cmd query "check network status"
Cross-binary workflow: ai-admin + ai-cmd + ai-scheduler
Automate this with ai-scheduler:
# Weekly maintenance on Sundays at 4 AM
ai-scheduler add --name "weekly-backup" \
--command "ai-admin db backup --output ./backup-weekly.db" \
--cron "0 4 * * 0"
ai-scheduler add --name "weekly-optimize" \
--command "ai-admin tune optimize" \
--cron "0 5 * * 0"
ai-scheduler add --name "weekly-cleanup" \
--command "ai-admin tune cleanup --days 30" \
--cron "0 6 * * 0"
Scenario 3: Recovery After Database Corruption
When the AI database becomes corrupted, restore from backup and rebuild.
# Step 1: Assess the damage
ai-admin diagnostics --full --json
# Step 2: If integrity check fails, restore from backup
ai-admin db restore --backup ./backup-20260209.db
# Step 3: Run migrations to ensure schema is current
ai-admin db migrate
# Step 4: Verify integrity after restore
ai-admin db integrity-check
# Step 5: Rebuild indexes
ai-admin tune rebuild-index
# Step 6: Retrain the model (embeddings may need refresh)
sudo ai-trainer train --data ./data/training-data.json
# Step 7: Validate the model
ai-trainer validate --test-data ./data/test-commands.json
# Step 8: Verify ai-cmd works end-to-end
ai-cmd query "check system health"
# Step 9: Check accuracy is restored
sudo ai-learner analyze
Cross-binary workflow: ai-admin + ai-trainer + ai-cmd + ai-learner
Scenario 4: Investigating AI Performance Issues
When ai-cmd accuracy drops, use ai-admin to check if the database is the root cause.
# Step 1: Run full diagnostics
ai-admin diagnostics --full
# Step 2: Check database integrity (corruption can cause bad predictions)
ai-admin db integrity-check
# Step 3: View database statistics
ai-admin db info --json
# Step 4: Optimize database if fragmented
ai-admin tune optimize
# Step 5: Analyze learning trends
sudo ai-learner analyze
# Step 6: Rebuild indexes if database issues detected
ai-admin tune rebuild-index
# Step 7: If accuracy is still low, consider full retraining
sudo ai-trainer train --data ./data/training-data.json
# Step 8: Generate a report for tracking
sudo ai-learner report
Cross-binary workflow: ai-admin + ai-learner + ai-trainer
Scenario 5: Pre- and Post-Training Database Care
Always prepare the database before training and clean up after.
Before Training
# Backup before any training operation
ai-admin db backup --output ./pre-training-$(date +%Y%m%d).db
# Check integrity to avoid training on corrupt data
ai-admin db integrity-check
# Optimize for best training performance
ai-admin tune optimize
After Training
# Rebuild indexes after training writes new data
ai-admin tune rebuild-index
# Clean up temporary data (keep last 90 days)
ai-admin tune cleanup --days 90
# Run diagnostics to verify health
ai-admin diagnostics --full
# Create post-training backup
ai-admin db backup --output ./post-training-$(date +%Y%m%d).db
Cross-binary workflow: ai-admin + ai-trainer
Scenario 6: Automated Maintenance Pipeline
Set up ai-scheduler to automate all database maintenance tasks.
# Daily: Database integrity check (1:00 AM)
ai-scheduler add --name "daily-integrity" \
--command "ai-admin db integrity-check" \
--cron "0 1 * * *"
# Weekly: Backup + optimize (Sundays 3:00-4:00 AM)
ai-scheduler add --name "weekly-backup" \
--command "ai-admin db backup --output ./backup-weekly.db" \
--cron "0 3 * * 0"
ai-scheduler add --name "weekly-optimize" \
--command "ai-admin tune optimize" \
--cron "0 4 * * 0"
# Monthly: Cleanup + rebuild indexes (1st of month 5:00-6:00 AM)
ai-scheduler add --name "monthly-cleanup" \
--command "ai-admin tune cleanup --days 30" \
--cron "0 5 1 * *"
ai-scheduler add --name "monthly-rebuild" \
--command "ai-admin tune rebuild-index" \
--cron "0 6 1 * *"
# Verify all scheduled tasks
ai-scheduler list
Cross-binary workflow: ai-admin + ai-scheduler
Complete automation (combine with learning pipeline):
# Full daily pipeline: Integrity → Learn → Monitor
# 1:00 AM - Database integrity check
# 2:00 AM - Incremental learning
# 3:00 AM - Security checks
ai-scheduler add --name "integrity" --command "ai-admin db integrity-check" --cron "0 1 * * *"
ai-scheduler add --name "learning" --command "ai-learner learn" --cron "0 2 * * *"
ai-scheduler add --name "security" --command "health-control sec-score" --cron "0 3 * * *"
Related Workflows
- Database Maintenance & Health — Weekly and recovery workflows
- Troubleshooting AI Accuracy — Using diagnostics to find root causes
- ai-trainer — Pre/post-training database care
- ai-learner — Learning depends on healthy database
- Full CLI Reference: ai-admin commands
Troubleshooting
| Problem | Cause | Solution |
|---|---|---|
| "Database locked" error | Another AI process is using the DB | Stop other ai-* processes, then retry |
| Integrity check fails | Corrupt database | Restore from backup: ai-admin db restore --from <backup-path> |
| Diagnostics show warnings | Missing tables or stale data | Run ai-admin db migrate then ai-admin tune optimize |
| Backup fails | Insufficient disk space or permissions | Check free space and run with sudo |
| Migration errors | Version mismatch | Ensure all AI binaries are the same version (check with -e) |