ALTER DBA ADD AGENT: Practical AI for Database Professionals

If you’re a SQL Server DBA who’s heard the buzz about AI but isn’t sure what it means for your daily work, this series is for you. No marketing fluff, no hand-waving — just a working DBA showing what happens when you point an AI coding agent at real database problems.

Each post covers a specific task area with real prompts, real output, and real tradeoffs. Start anywhere that matches what’s on your plate today.

The Series

Getting Oriented

1. The DBA’s Blind Spot: Why AI Coding Agents Are Coming for Your Workflow — Code completion vs. coding agents, and why the difference matters more than you think.

2. What Can an AI Coding Agent Actually Do for a DBA? — A tour of every task area where these tools add value, from T-SQL to security audits.

3. Getting Started: Your First Hour with GitHub Copilot CLI — Installation, first prompts, and what to expect when you fire it up.

Daily Work

1. Writing T-SQL with an AI Partner — Generating, reviewing, and refactoring T-SQL with an agent that reads your schema.

   • AI-Assisted Implicit Join Conversion — Converting legacy implicit joins to ANSI syntax
   • AI-Assisted Window Function Development — Building and debugging window functions
   • AI-Assisted Cursor-to-Set-Based Conversion — Replacing cursors with set-based operations
   • AI-Assisted Parameter Sniffing Diagnosis — Diagnosing and fixing parameter sniffing issues
2. Automating Server Health Checks and Inventory Scripts — Building the scripts that keep your fleet healthy, faster than you’d write them alone.
   • AI-Assisted Backup Status Audit — Auditing backup status across your fleet
   • AI-Assisted Version and Patch Inventory — Building version inventories
   • AI-Assisted AG Topology Mapping — Documenting AG configurations
   • AI-Assisted Compatibility Level Audit — Finding compat level mismatches
3. PowerShell Automation: Backups, Maintenance, and AG Management — AG failover scripts, backup validation, and maintenance automation with AI assistance.
   • AI-Assisted Backup Automation with PowerShell — Building robust backup scripts
   • AI-Assisted AG Failover Runbook — AG failover procedures
   • AI-Assisted DBCC CHECKDB Scheduling — Scheduling integrity checks
   • AI-Assisted Patching Pre/Post Checks — Patch readiness automation
4. Understanding Unfamiliar Code: Reverse-Engineering Legacy Procedures — That 2,000-line stored procedure nobody wants to touch? The agent will read it.
   • AI-Assisted Dynamic SQL Dissection — Understanding complex dynamic SQL
   • AI-Assisted Dependency Mapping — Mapping object dependencies
   • AI-Assisted Cursor Elimination — Step-by-step cursor replacement

Troubleshooting

1. Wait Stats, Deadlocks, and Blocking Chains: AI-Assisted Diagnosis — Turning raw wait stats and deadlock graphs into actionable explanations.
   • AI-Assisted CXPACKET Diagnosis — Diagnosing parallelism waits
   • AI-Assisted Deadlock Analysis — Reading deadlock graphs
   • AI-Assisted Blocking Chain Triage — Resolving blocking chains
   • AI-Assisted Memory Grant Analysis — Diagnosing memory grant issues
2. Incident Response: Root Cause Analysis with an AI Partner — When the pager goes off at 2 AM, having a tireless partner changes the equation.

Security and Compliance

1. Security Audits: Finding What You Missed — Orphaned users, permission sprawl, and the gaps that accumulate over years.
   • AI-Assisted Orphaned User Cleanup — Finding and fixing orphaned users
   • AI-Assisted Permission Sprawl Analysis — Auditing permission creep
   • AI-Assisted SQL Injection Scanning — Finding injection vulnerabilities
   • AI-Assisted Compliance Checklists — SOX/PCI/HIPAA audit automation

Migration and Modernization

1. Migration Planning: Compatibility Checks and Deprecated Features — Scanning for deprecated syntax, compatibility issues, and migration blockers.
   • AI-Assisted Deprecated Feature Scanning — Finding deprecated features
   • AI-Assisted Azure SQL Feasibility Assessment — Evaluating Azure migration readiness
   • AI-Assisted Post-Migration Validation — Verifying migration success

Monitoring

1. AI-Native Monitoring: PerformanceMonitor, PerformanceStudio, and the MCP Revolution — Tools built from the ground up to work with AI agents, featuring Erik Darling’s PerformanceMonitor and PerformanceStudio.

2. Building Custom Monitoring Queries and Alerts — When off-the-shelf monitoring doesn’t fit, build exactly what you need.

   • AI-Assisted Long-Running Transaction Alerts — Monitoring open transactions
   • AI-Assisted Blocking Alerts — Building blocking chain alerts
   • AI-Assisted AG Health Monitoring — Availability Group health checks
   • AI-Assisted Backup Failure Alerts — Backup monitoring automation

Infrastructure and Process

1. Version Control and CI/CD: Unlocking What the Agent Can Actually Do — AI agents get dramatically more useful when your database code is in a repo.

2. Teaching AI Your Environment: Custom Instructions and Context — Making the agent deeply effective by teaching it your standards, naming conventions, and environment.

3. AI-Assisted Pull Request Reviews for Database Code — Schema changes, deployment scripts, and permission audits — reviewed before the senior DBA even looks.

The Bigger Picture

1. The AI-Augmented DBA Team: Mentoring and Knowledge Transfer — What changes when the whole team has AI partners, not just one person.

2. How This Series Was Written: A DBA and an AI Walk Into a Terminal — The meta post. Every word in this series was co-authored with the tools it describes. Here’s how.

For Junior DBAs

• A Junior DBA’s Field Guide to This Series — New to the profession? This companion guide maps the vocabulary walls, production safety gaps, and confidence calibration challenges you’ll hit. Senior DBAs: share this with your juniors before pointing them at the series.

Source Materials

The complete working directory for this series — every draft, editorial decision, and iteration — is available on GitHub:

github.com/HannahVernon/dba-blog