Building AI systems in a chat window is easy. Running them in production is a different experience entirely. Here are six lessons I learned the hard way.
1. Silent Failures Are the Most Dangerous
When an AI system fails loudly - an error message, a crashed process, a 500 response - you know something is wrong. Silent failures are worse. The system appears to work, but it’s producing wrong output, returning empty results, or skipping steps without telling you.
Build explicit error handling. Log everything. If a semantic search returns zero results, that should throw a visible alert, not silently return an empty list.
2. The Context Pool Needs Maintenance
Your vector database is not a write-once archive. It’s a living system. When your knowledge changes - a new product, a revised policy, an updated framework - the old embeddings need to be updated or replaced. Stale embeddings produce stale output.
Build a maintenance process. Schedule regular audits of your context pool. Flag records that haven’t been updated in 90 days for review.
3. Test in Isolation Before Connecting to Production
Every new agent instruction, every new tool integration, every new workflow should be tested in an isolated environment before it touches production data. One bad instruction in a production agent can corrupt data, send wrong emails, or publish incorrect content before you catch it.
4. SSL and Proxy Configuration Is a Minefield
If you are running AI services behind a reverse proxy (like Nginx) or through a CDN (like Cloudflare), SSL configuration errors will cause mysterious failures. The most common: Cloudflare on Flexible SSL mode with an origin server that forces HTTPS. Infinite redirect loop. Always use Full (strict) mode when your origin has a valid certificate.
5. Scheduled Jobs Drift
Cron jobs and scheduled tasks work perfectly when you set them up. Three months later, they silently stop running because a dependency changed, a credential expired, or a server was rebooted. Build monitoring for your scheduled jobs. If a job hasn’t run in 25 hours, you should know about it.
6. Document Everything
The system you build today will be maintained by a future version of you who has forgotten how it works. Document every integration, every credential, every architectural decision, and every known failure mode. Your future self will thank you.
Related reading:
- Cloudflare SSL Redirect Loop Fix
- AI Ecosystem Security Principles
- How to Build Your First Agent Loop
Found this useful? Check out the Learn section for structured micro-lessons on building AI systems, or read more on the blog for more practical guides.
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Stay curious, my AI friend. It's the secret sauce - think like you are seven. - Ryan