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Autonomous agentic workflows for drug discovery are only viable when foundational models achieve high reliability, as agents inherently amplify underlying prediction errors.

Impact: Investing in robust base models before deploying agentic orchestration prevents costly workflow failures and ensures chemist-grade output quality.

— from AI-Driven Drug Discovery: Precision, Agents, and Pharma Partnerships · Latent Space: The AI Engineer Podcast· Jul 01, 2026