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The shift from static software components to adaptive, goal-directed agents demands a new engineering framework to ensure reliability and trust in AI systems.
AAFLOW+ slashes multi-agent compute costs by over 7x while enabling zero-copy context sharing, revolutionizing how agents manage shared state.
Agentic workflows can be sped up by 4.6x, not through faster LLMs, but by optimizing data flow and communication between components.
MLP-based models dominate in outbreak forecasting, but traditional statistical methods shine when it matters most鈥攂efore an outbreak peaks.
