Production AI agents are failing not due to model hallucinations, but because of rate limits imposed by LLM providers. These limits, often overlooked in demos, become a critical bottleneck in real-world applications where a single user action can trigger dozens of concurrent model calls and retries. Addressing this requires a focus on capacity engineering, including budgeting, backpressure, and caching, rather than solely on prompt engineering. AI
IMPACT Highlights that capacity engineering, not just prompt engineering, is crucial for reliable AI agent deployment.
RANK_REASON The article discusses a common production failure mode for AI agents, focusing on rate limits rather than model capabilities, offering insights and solutions.
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