What I learned after a weekend of AI troubleshooting gone wrong (8 key lessons)

Redgate's post “What I learned after a weekend of AI troubleshooting gone wrong” highlights eight key lessons for getting more reliable results from AI coding agents, emphasizing the pitfalls of unchecked prompt chaining, missing version control, and silent API rate‑limit failures. For website owners this means that AI‑driven development tools—such as code‑completion bots integrated into CI pipelines or on‑site documentation generators—can generate unexpected traffic spikes, malformed requests, and even accidental data exposure when they malfunction. The article’s recount of a bot that repeatedly hit a mis‑configured endpoint underscores the risk of uncontrolled AI agents flooding your server logs, skewing analytics, and triggering security alerts. **Actionable steps:** 1. **Add a dedicated entry in your llms.txt** for any AI coding agent you expose (e.g., `User‑Agent: RedgateAI‑Coder/1.0; Disallow: /admin/*`) to ensure crawlers respect sensitive paths. 2. **Implement real‑time bot traffic monitoring** in llmscentral.com, flagging sudden surges from the same User‑Agent string and automatically throttling requests to prevent overload. 3. **Version‑lock your AI API keys** and log each request’s timestamp and payload; then cross‑reference those logs with your llms.txt access reports to quickly pinpoint which AI agent caused a spike and isolate it.