What is Agentic Machine Learning?

Agentic machine learning uses agents to automate the iterative model-development cycle of proposing hypotheses, implementing changes, evaluating results against metrics, and retaining or discarding modifications while humans set objectives and approve production changes. It can increase experimentation throughput, investigate more prediction failures, and help teams support additional models without proportional staffing growth, but differs from AutoML by generating or reframing hypotheses rather than only searching predefined spaces. Its central risks are not simply coding errors but misleading evaluation results caused by feature leakage, point-in-time join mistakes, contamination of held-out data, unavailable production features, and latency constraints, all of which can improve offline metrics without producing real-world gains. Trustworthy deployment therefore requires human review calibrated to risk, evaluation of both model outcomes and agent trajectories, reproducible traces, and access to the same source data and feature definitions used in production. The piece argues that a data layer providing point-in-time-correct training data, shared training-serving feature definitions, lineage, governance, and federated access to operational sources is essential, presenting Chalk’s Context Engine as infrastructure intended to support these requirements.

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