How I Built an AI Agent That Monitors My SaaS and Fixes Issues Before Users Notice

Alan P. Oliver — Thu, 19 Mar 2026 19:45:07 +0000

My SaaS has 2,400 users. I'm a solo dev. Two months ago I built an AI agent that monitors production and automatically fixes common issues. It's saved me from at least 3 outages.

The Architecture

Vercel (Next.js) → Sentry (errors) → Webhook → AI Agent → GitHub PR / Hotfix

The agent runs as a Vercel serverless function triggered by Sentry webhooks. When an error hits a threshold (>5 occurrences in 10 minutes), the agent:

Fetches the error details + stack trace from Sentry
Pulls the relevant source files from GitHub
Analyzes the root cause
Generates a fix
Runs the test suite against the fix
If tests pass, opens a PR (or auto-merges for known safe patterns)

What It's Fixed Automatically

Database connection pool exhaustion — It detected the leak pattern and added proper connection cleanup
Rate limit errors from a third-party API — Added exponential backoff that I'd been meaning to implement
Null pointer in a new feature — Added a guard clause and a comment "auto-fixed: missing null check on user.subscription"

What It Can't Fix

Business logic bugs (it doesn't know what the product should do)
Performance issues (it can spot N+1 queries but won't restructure your data model)
Anything requiring a database migration

The Cost

About $30/month in AI API costs. Compared to being woken up at 3am or losing users to preventable bugs, it's the best $30 I spend.

Key Lessons

Start narrow. Don't try to auto-fix everything. Start with null pointer errors and connection issues.
Always require tests to pass. The agent can't merge if it can't prove the fix is safe.
Log everything. I have a full audit trail of every auto-fix. This saved me when one "fix" introduced a subtle regression.

Solo devs: you need this. You can't monitor production 24/7, but an AI agent can.