Textual Gradients and Workflow Evolution: How AdaptFlow Reinvents Meta-Learning for AI Agents
From Static Scripts to Living Workflows The AI agent world has a scaling problem: most automated workflow builders generate one static orchestration per domain. Great in benchmarks, brittle in the wild. AdaptFlow — a meta-learning framework from Microsoft and Peking University — proposes a fix: treat workflow design like model training, but swap numerical gradients for natural language feedback. This small shift has a big implication: instead of re-engineering from scratch for each use case, you start from a meta-learned workflow skeleton and adapt it on the fly for each subtask. ...