You Fixed the Rate Limits. Now Your Agent Fails Quietly.
This article discusses a critical trade-off in building reliable AI agents: fixing availability issues can introduce subtle correctness problems. While rate limits cause loud, visible failures, implementing retries, fallbacks, and caching to maintain uptime can lead to agents acting on stale data or outputs from different models. The author proposes treating availability and correctness as separate gates, ensuring that irreversible actions are only taken with outputs that are both available and verifiably correct, rather than simply confident. AI
IMPACT Highlights a critical challenge in deploying AI agents, emphasizing the need for robust correctness checks alongside availability measures.