A coordinated multi-agent communication pattern with shared or persistent state best satisfies the requirement because the detector, redactor, and auditor must exchange information and maintain the state of a multi-step workflow.
AWS describes multi-agent collaboration as providing “a centralized mechanism for planning, orchestration, and user interaction.” A supervisor can coordinate multiple specialized agents, assign work, route information, and execute a plan across collaborator agents.
In this scenario, the detector first identifies problematic content and produces details such as the affected segment and violation type. The redactor must receive those details to modify the appropriate content. The auditor must then receive the resulting context and verify that the required moderation action occurred. This requires communication plus persis tent workflow state so that downstream agents retain the relevant information and understand the state of earlier processing.
AWS AgentCore Memory documentation explicitly identifies multi-agent systems as a memory use case, explaining that a team of agents can share memory to synchronize information. AWS also describes workflow agents as using memory to track the status of individual steps and maintain progress through a multi-step process.
Amazon Bedrock multi-agent functionality additionally supports sharing conversational history with collaborator agents, allowing relevant runtime context to flow from supervisor to collaborator.
Model Context Protocol (MCP) primarily standardizes how AI systems connect to external tools, services, and contextual resources. MCP by itself does not define the detector → redactor → auditor processing sequence required here.
Video compression concerns media transport or storage efficiency, not agent coordination. Adjusting inference batch size concerns performance and throughput rather than workflow state or inter-agent communication.
Strictly speaking, orchestration establishes task ordering while memory preserves shared state. Of the choices provided, option B is the only one representing the combination of agent communication and persistent context required for the workflow.
Therefore, B. Multi-agent communication patterns that use persistent memory is the correct answer.
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