Production-ready AI agent engineering requires frameworks like LangChain and Haystack that prioritize modularity, observability, and robust error handling over mere convenience. Cross-model benchmarks such as HumanEval and AgentBench are useful for assessing general capabilities but often overlook operational concerns like latency and cost. Developers must focus on state management, retry logic, and cost control to ensure reliable deployment in real-world scenarios.
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