Google and AWS split the AI agent stack between control and execution

The era of enterprises stitching together prompt chains and shadow agents is nearing its end as more options for orchestrating complex multi-agent systems emerge. As organizations move AI agents into production, the question remains: "how will we manage them?"

Google and Amazon Web Services offer fundamentally different answers, illustrating a split in the AI stack. Google’s approach is to run agentic management on the system layer, while AWS’s harness method sets up in the execution layer.

The debate on how to manage and control gained new energy this past month as competing companies released or updated their agent builder platforms—Anthropic with the new Claude Managed Agents and OpenAI with enhancements to the Agents SDK—giving developer teams options for managing agents.

AWS with new capabilities added to Bedrock AgentCore is optimizing for velocity—relying on harnesses to bring agents to product faster—while still offering identity and tool management.

Meanwhile, Google’s Gemini Enterprise adopts a governance-focused approach using a Kubernetes-style control plane. Each method offers a glimpse into how agents move from short-burst task helpers to longer-running entities within a workflow.

Upgrades and umbrellas

To understand where each company stands, here’s what’s actually new.

Google released a new version of Gemini Enterprise, bringing its enterprise AI agent offerings—Gemini Enterprise Platform and Gemini Enterprise Application—under one umbrella. The company has rebranded Vertex AI as Gemini Enterprise Platform, though it insists that, aside from the name change and new features, it’s still fundamentally the same interface.

On the other hand, AWS added a new managed agent harness to Bedrock Agentcore. The company said that the harness “replaces upfront build with a config-based starting point powered by Strands Agents, AWS’s open source agent framework.” Users define what the agent does, the model it uses and the tools it calls, and AgentCore does the work to stitch all of that together to run the agent.

Agents are now becoming systems

The shift toward stateful, long-running autonomous agents has forced a rethink of how AI systems behave. As agents move from short-lived tasks to long-running workflows, a new class of failure is emerging: state drift. As agents continue operating, they accumulate state—memory, too, responses and evolving context. Over time, that state becomes outdated, making the agent more vulnerable to inconsistencies.

The new AI stack

What’s becoming increasingly clear is that the AI stack is separating into distinct layers. AWS, Anthropic, and OpenAI optimize for faster deployment. Google offers a centralized control panel to manage identity, enforce policies and monitor long-running behaviors. Enterprises likely need both to balance experimentation with trust and risk management.