Most AI workflows and agents don't fail because of the model. They fail because of everything around it: data that hasn't been checked, jobs that run in the wrong order, race conditions between services, and no audit trail once the agent has acted. An agent that reasons over incomplete data won't throw an error. It will give you a confident recommendation built on bad inputs.

In our previous webinar, Orchestrating AI at Scale, we looked at why enterprise AI stalls at scale rather than at the model. This session goes a level deeper, into architecture and a live build. BMC and AWS solution experts will show how the Control-M integration with Amazon Bedrock and other AWS services bring AI, data, and cloud services together into a single, governed end-to-end workflow.

You'll leave knowing how to gate AI on data readiness, retry or stop a pipeline when a step fails, set SLAs across the whole AI lifecycle, and build a pipeline that compliance teams can audit.

Key Takeaways:

-Where AI pipelines break in production: unchecked data, timing gaps and failures nobody sees, and how orchestration closes each one.

-How the integration works: the Control-M and Amazon integration capabilities and patterns

-A reference architecture you can reuse: seven steps, Snowflake to Bedrock to QuickSight, with least-privilege access (IAM), encryption (KMS) and a full audit log.