AWS Step Functions now integrates with Amazon Bedrock AgentCore (currently in preview) to allow AI agent reasoning steps within automated workflows. This enables teams to embed LLM-based tasks such as document classification and data extraction directly into orchestrated pipelines, with parallel execution and human approval gates. Audit trails are available via CloudWatch, capturing agent inputs, outputs, and token usage.

Architect’s Take: Review IAM permissions granted to Step Functions execution roles that invoke AgentCore harnesses — ensure least-privilege policies are applied, particularly around model invocation and tool access. Treat human approval steps as a mandatory control for any agentic action with write or destructive scope, and validate that CloudWatch audit logging is enabled before promoting any AgentCore-integrated workflow to production.

Original advisory: AWS Step Functions adds AgentCore-powered agentic reasoning step

Amazon SageMaker Studio’s quick setup time has been reduced from over two minutes to under twenty seconds. New Studio environments now automatically receive a managed IAM policy granting serverless model customisation permissions, including fine-tuning, evaluation, and deployment to SageMaker or Bedrock endpoints. This reduces friction for ML practitioners but introduces pre-configured IAM permissions that security teams should review.

Architect’s Take: Review the scope of the automatically attached AmazonSageMakerModelCustomizationCoreAccess managed policy against your least-privilege baselines — auto-provisioned IAM policies with deployment permissions to Bedrock and SageMaker endpoints may exceed what individual users or teams require. Consider whether your landing zone or Service Control Policies should restrict or audit automatic policy attachment in SageMaker Studio environments.

Original advisory: Amazon SageMaker Studio now sets up in seconds with model customization ready from the start