🤖 Cloud AI Agents: AWS, GCP, Azure Comparison

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The artificial intelligence landscape is rapidly transforming from generative AI, which primarily creates content, to agentic AI, which autonomously performs actions. This shift is driven by the need for tangible automation and complex problem-solving. Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure are heavily investing in comprehensive platforms for developing, deploying, and managing these intelligent agents. These platforms represent "the next frontier of enterprise software, promising to automate intricate workflows, enhance productivity, and unlock new business value."

This briefing document provides a comparative analysis of AWS, GCP, and Azure's agentic AI offerings, highlighting their architectural philosophies, core features, and enterprise readiness. Each provider has distinct strategic approaches tailored to their strengths:

• AWS prioritizes modularity and choice with Amazon Bedrock Agents (managed) and Amazon Bedrock AgentCore (framework-agnostic runtime).
• GCP focuses on an open, interoperable ecosystem, promoting the open-source Agent Development Kit (ADK) and standards like the Agent2Agent (A2A) protocol within its Vertex AI Agent Builder suite.
• Microsoft Azure adopts an enterprise-first, deeply integrated "factory" approach with Azure AI Foundry, leveraging its dominant position in corporate workflows and developer tooling.

The "best" platform is contingent on an organization's existing cloud footprint, developer culture, security posture, and strategic priorities. The selection of an agentic AI platform is a "decision of profound strategic importance," impacting future automation, data strategy, security, and competitive differentiation.