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 the Agentic Decision Engine (ADE), a multi-agent, natural language interface over CDE. A supervisor agent routes each question to a specialist agent, the specialist retrieves context from the relevant system, and the user receives an answer without navigating the operational stack. In its first phase, ADE resolves about 80 percent of user questions without an engineer in the loop.

ADE runs on Amazon Bedrock AgentCore , an AWS service to build, connect, and optimize agents at scale with any framework or model. AgentCore let the team focus on agent behavior instead of building and operating its own agent hosting, session isolation, and tool access layer.

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