How Bayer Crop Science built an agentic decision engine on Amazon Bedrock AgentCore with AWS Unified Operations | Amazon Web Services
Plant breeding decisions depend on data that spans genomics, field trials, imagery, environmental conditions, and model outputs. Bayer Crop Science runs hundreds of machine learning (ML) model configurations against this data across crops, regions, and teams, and each run needs to be repeatable and governed. To meet that requirement, Bayer built the Connected Decision Engine (CDE), a system for deploying, orchestrating, monitoring, and reusing ML workflows. CDE gives breeders, analysts, researchers, data scientists, and data engineers a consistent way to run analytics while preserving execution history and operational controls.
As adoption grew, the bottleneck moved from running workflows to understanding them. Explaining a result meant piecing together context from APIs, documentation, metadata stores, dashboards, and logs. A user who wanted to know why a workflow failed, what a model did, or where a result came from still had to know which system to inspect. To close that gap, Bayer built...
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