Best Practices for Building Agentic AI Systems: What Actually Works in Production

Production agentic AI systems work best with a two-tier architecture where primary agents maintain context and orchestrate work, while stateless subagents execute single tasks in complete isolation without memory or conversation history. This design enables parallel execution, predictable behavior, and simple caching by treating each subagent call as a pure function with deterministic inputs and outputs. Task decomposition should use vertical sequencing for dependent operations and horizontal parallelization for independent work, with mixed approaches combining both strategies for complex workflows like multi-phase feedback analysis.

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