I wanted to move beyond using AI for just writing code or content and see if it could manage complex, specialized systems. This led me to build my own AI database assistant, and it's already handling about 80% of my routine DBA tasks via natural language.

This isn't just a fancy chat interface. It's a command center where a Mistral-powered AI agent connects to my PostgreSQL data (via Postgres MCP) and gives me instant health reports and executable optimization commands.

Here’s the stack and how it works:

The Brain: I'm using an open-source Mistral model. Its reasoning capability is key—it doesn't just fetch data; it understands the intent behind a request like "Why is the database slow?"

The Bridge: The agent accesses the database through a 'Postgres MCP' (Monitoring and Control Plane) bridge, all running in Docker. This gives the AI secure access to the necessary metrics and logs without direct, risky DB exposure.

The "Wisdom": The system combines traditional DBA knowledge (like analyzing pg_stat_statements outputs) with the speed of AI.

The result? I no longer spend hours manually parsing query stats. I can just ask:

"Find the top 5 slowest queries from the last 24 hours."

"What's the index hit rate on the users table?"

"Simulate a new index on orders(customer_id). "

For that last part, it even uses hypopg to simulate index recommendations in seconds without locking tables.

I've essentially created a "turnkey" system that takes my natural language commands, diagnoses performance issues, and suggests concrete optimizations. It feels like a real example of how AI agents will merge with deep technical domains.

Happy to answer any questions about the setup.

https://www.bigdba.com/postgresql/2267/ai-driven-postgresql-performance-tuning-the-open-source-llm-and-mcp-guide/