How to Build Reliable AI Agents: 5 Engineering Patterns from a Production System
Summary
Building reliable AI agents in a complex production environment requires more than sophisticated models — it demands solving fundamental architecture challenges. The key insight is separating ambiguous reasoning tasks from deterministic computation tasks, leveraging proven algorithms for consistency, managing context carefully, and embedding strict verification rather than relying solely on prompts or multi-agent setups. Salesforce teams can apply these patterns to build AI-driven automation with predictable outcomes, especially when working with distributed configurations or infrastructure changes. This approach highlights that reliability stems from solid system design around AI, not just prompt engineering.
Takeaways
- Separate reasoning from deterministic computation to improve agent reliability.
- Use deterministic algorithms for optimization and scheduling tasks, not LLMs.
- Manage context as an architectural concern to prevent prompt overload.
- Validate agent outputs with objective, deterministic checks, not just reviewer agents.
- Focus on improving the system harness before revising prompts for failures.
By Tuhin Kanti Sharma and Chirag Ramesh Hegde. If you’ve built an AI agent that works perfectly in demos but becomes unpredictable in production, you’ve probably already discovered that reliability is much harder than capability. We ran into that problem while trying to build an agent to automate capacity optimization worth millions of dollars in potential cloud savings. Cloud economics data showed that 86% of containers across our fleet were running at less than 20% of their requested CPU, leaving roughly half of our provisioned capacity sitting idle. Recovering those savings should have been straightforward, except for one problem: nobody could reliably determine where many of the CPU settings actually came from. Hyperforce has evolved over the years, which has led to scattered resource definitions across Terraform, Helm and Structured Configuration systems. These are further complicated by environment and region-specific overrides.