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Agentic AI use case: Classify multimodal data | Cloud Architecture Center | Google Cloud Documentation
Google Cloud's architecture for multi-agent AI systems analyzes multimodal data, producing high-confidence classifications by cross-validating with historical data.
Stargo's Stardox platform can leverage multi-agent AI systems to transform unstructured data into high-confidence, actionable insights.
Executive Summary
This document provides a high-level architecture for a multi-agent AI system deployed on Cloud Run that analyzes disparate multimodal data and produces a high-confidence classification. This approach cross-validates fragmented media by matching live data against historical ground truth to produce grounded, verifiable insights. The intended audience includes architects, developers, and administrators who build and manage AI infrastructure and applications in the cloud. The document assumes a foundational understanding of AI agents and models. It lists code samples for building and deploying multi-agent AI systems. The architecture uses a parallel agent design pattern to coordinate independent analysis on multimodal data to produce a single classification. The root agent, deployed on a Cloud Run service, handles requests by gathering environment configurations, validating user input, and saving resource paths in a shared session state. This eliminates redundant calls to fetch state data and decreases latency. The root agent uses Gemini on Vertex AI to interpret requests and distribute tasks to specialized subagents running in parallel.
Source: Google Cloud Documentation
Original Article: https://docs.cloud.google.com/architecture/agentic-ai-classify-multimodal-data
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