How to Build Reliable AI Agents: 5 Engineering Patterns
This title could be clearer and more informative.Try out Clickbait Shieldfor free (5 uses left this month).
Building AI agents that work in demos but fail in production is a common problem. A Salesforce engineering team shares five patterns learned while building an agent to automate cloud capacity optimization. The core insight: most agent failures are architecture failures, not model failures. The patterns cover separating reasoning from deterministic computation (using Integer Linear Programming for optimization), treating context management as an architectural concern rather than a prompt design issue, building verification hierarchies around deterministic checks (CI, linters, compilers) instead of reviewer agents, and improving the system harness before rewriting prompts. The team found that reliability improved dramatically once they stopped asking LLMs to perform work better suited to deterministic systems.
