We've deployed AI agents for dozens of businesses. Here are the patterns that kill most installations before they ever reach production.

1. Running agents on shared infrastructure

Most tutorials have you spinning up agents on a shared cloud instance or a third-party SaaS. The moment your agent handles customer data, PII, or internal docs, you've created a liability.

Fix: Deploy on infrastructure you control. Dedicated instance, your keys, your network. Non-negotiable if you're handling anything beyond toy data.

2. No restart or health-check strategy

Agents crash. APIs rate-limit. Models hallucinate and enter loops. If your agent goes down at 2am and nobody notices until Monday, you've lost days of work.

Fix: Wrap every agent in a process supervisor with health checks, automatic restarts, and alerting. Treat agents like production services, not scripts.

3. Skipping prompt versioning

You tweak a system prompt, deploy it, and suddenly your agent is doing something completely different. No rollback, no audit trail.

Fix: Version-control your prompts the same way you version code. Tag releases. Keep a changelog. You'll thank yourself when something breaks.

4. One mega-agent instead of orchestrated specialists

A single agent trying to handle email triage, CRM updates, Slack notifications, and document generation will be mediocre at all of them.

Fix: Use multi-agent orchestration. Each agent handles one domain well. A coordinator routes tasks. Simpler to debug, easier to scale, better results.

5. No post-launch optimization window

Day-one performance is never peak performance. Real usage patterns are different from test scenarios. If you "set and forget," your agents degrade.

Fix: Build in a 30-day optimization window after launch. Monitor real throughput, error rates, and output quality. Adjust prompts and orchestration based on actual data.

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We handle all five of these at ClawBuildr. If you want production-ready AI agents installed on your infrastructure in one week, check out our installation service.