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A logistics company is building an agentic GenAI-powered solution to automate freight optimization.

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A logistics company is building an agentic GenAI-powered solution to automate freight optimization. The solution must retrieve data in real time from multiple internal and external systems. The solution must include a human-in-the-loop approval step before the optimization process is finished. The solution must support modular growth as the number of integrations and amount of logic increases.

Which solution will meet these requirements with the LEAST operational overhead?

A.

Use an Amazon SageMaker AI endpoint that hosts a large language model (LLM) that directly calls all internal databases and external APIs. Use a custom web application that provides a UI to implement the human-in-the-loop review step.

B.

Build a hierarchical system by using the Strands Agents SDK and Amazon Bedrock AgentCore. Configure a coordinating agent to delegate tasks to multiple specialized agents. Use MCP to facilitate inter-agent messaging. Use AWS Step Functions to implement a human-in-the-loop approval step.

C.

Use AWS Glue to aggregate operational data into Amazon S3. Use Amazon Athena to query the data. Invoke an AWS Lambda function to generate route assignments. Use Amazon SNS to send notifications to supervisor agents.

D.

Use a single Amazon Bedrock AgentCore agent with AWS Lambda-based tools to integrate with all internal and external systems. Use AWS Step Functions to orchestrate the approval workflow.

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