20d • General discussion
Most AI agents fail in production for one reason:
Teams optimize prompts before they optimize systems.
After reviewing dozens of real-world builds, the pattern is clear:
If you skip evals, memory architecture, and observability, your “AI assistant” becomes a fragile demo.
What actually works in production:
1) Evaluation loops
- Define success/failure criteria before shipping
- Track output quality over time, not one-off wins
2) Memory architecture
- Core facts (always available)
- Recent context (compressed)
- Semantic retrieval (long-term recall without context bloat)
3) Observability
- Tool-call logs
- Failure reasons
- Cost + latency per workflow
4) Governance
- Approval gates for external actions
- Tool allowlists
- Audit trail for every critical step
The market is moving from “Can it generate?” to “Can it operate reliably at scale?”
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Most AI agents fail in production for one reason:
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