Building safety tooling for risk-free AI tuning of Postgres | POSETTE: An Event for Postgres 2026

Author: Microsoft Developer - Bewertung: 0x - Views:5

Learn how safety tooling supports risk-free AI tuning of Postgres. Mohsin Ejaz (DBtune) discusses this in his talk “Building safety tooling for risk-free AI tuning of Postgres: Fast cars need fast brakes” at POSETTE: An Event for Postgres 2026. Abstract: Optimizing your database with AI is a tantalizing prospect, but how can we make sure to do this in a risk-free manner? In this talk, I will share my experience with building safeguards and guardrails for automated PostgreSQL tuning to help you sleep well at night — the better the safety net, the more freely we can let the agent work to improve the system.

I will walk through the tried and tested safety patterns: memory and performance monitoring, and validation techniques that ensure every change is safe. The goal is simple — get the performance gains you want while minimizing risk to your system. Whether you are considering automated tuning or building your own tools, building for safety should always be the highest priority.

Mohsin Ejaz is a Senior DevOps Engineer with 19 years of experience, including 17 years at EnterpriseDB. He specializes in PostgreSQL performance optimization and currently works as a DevOps and Benchmarking expert at DBtune, helping companies optimize their PostgreSQL infrastructure.

► Video chapters:
⏩ 00:00 – Music & introduction
⏩ 01:26 – The promise and fears around AI tuning
⏩ 03:35 – Why Postgres tuning is complex
⏩ 06:00 – Testing across environments and workloads
⏩ 07:26 – Five key challenges in AI tuning
⏩ 08:44 – Memory pitfalls and OOM risks
⏩ 12:54 – Exploration trade-offs and search strategy
⏩ 17:29 – Confidence and statistical validation
⏩ 20:30 – Parameter interactions and ripple effects
⏩ 22:47 – Evaluating AI tools without blind trust
⏩ 24:40 – Safety-first design principles and takeaways

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