Anthropic last week announced a new platform, Cloud Managed Agents, which aims to eliminate the more complex parts of AI agent deployment for enterprises and compete with existing orchestration frameworks.
Cloud managed agents are also an architectural shift: Enterprises, already burdened with orchestrating growing numbers of agents, can now choose to embed orchestration logic In the AI model layer.
While this comes with some potential benefits, such as speed (Anthropic proposes that its customers can deploy agents in days rather than weeks or months), it certainly also gives the model provider more control over the deployment and operation of the enterprise’s AI agent – in this case, Anthropic – potentially resulting in more "to close" For enterprise customers, they are left subject to Anthropic’s terms, conditions and any subsequent platform changes.
But perhaps it’s worth it for your enterprise, as Anthropic further claims that its platform “handles the complexity” by letting users define agent tasks, tools, and guardrails with a built-in orchestration harness, all without the need for sandboxing code execution, checkpointing, credential management, scoped permissions, and end-to-end tracing.
The framework manages state, execution graphs and routing and brings managed agents into the vendor-controlled runtime loop.
Even before the release of Cloud Managed Agents, new directional VentureBeat research revealed that Anthropic was gaining traction at the orchestration level as enterprises adopted its native tooling. Cloud Managed Agents represents a new effort by the firm to broaden its footprint as the orchestration method of choice for organizations.
Anthropic’s growing interest in orchestration
Orchestration has emerged as a critical segment for enterprises as they scale AI systems and deploy agentic workflows.
VentureBeat directional research of several dozen firms for the first quarter of 2026 found that enterprises mostly chose existing frameworks, such as Microsoft’s Copilot Studio/Azure AI Studio, with 38.6% of respondents in February reporting using Microsoft’s platform. VentureBeat surveyed 56 organizations with more than 100 employees in January and 70 organizations in February.
OpenAI is in second place with 25.7%. Both showed strong growth between the first two months of the year.
Anthropic, inspired by increased interest in its offerings like Cloud Code over the past year, is fighting back.
Anthropic tool-use and adoption of workflow APIs increased from 0% to 5.7% between January and February. This tracks closely with the growing adoption of Anthropic’s Foundation model, which shows that enterprises using the cloud turn to the company’s native orchestration tooling rather than adding third-party frameworks.
While VentureBeat conducted the survey ahead of the launch of Cloud Managed Agents, we can guess that the new tool will build on that development, especially if it promises a more simplified way to deploy agents.
collapse of outer orchestration layer
Enterprises may find a streamlined, internal harness for agents attractive, but it means giving up some control.
Session data is stored in a database managed by Anthropic, increasing the risk that enterprises are locked into systems operated by a single company. This may be less desirable for some companies and may compete with their desires to move away from the locked-in software-as-a-service (SaaS) applications in the existing stack that many hope AI will facilitate.
The specter of vendor lock-in means that agent execution becomes more model-driven rather than direct by the organization, it occurs in an environment that enterprises do not fully control, and behavior becomes harder to guarantee.
It also opens up the possibility of giving conflicting instructions to agents, especially if the only way for users to have control over agents is to prompt them with more context.
Agents can have two control planes: one defined by the enterprises’ orchestration system through instructions and the other as an embedded skill from the cloud runtime.
This can pose a problem for highly sensitive and regulated workflows such as financial analysis or customer-facing tasks.
Pricing, Control and Competitive Set
It’s one thing to easily balance the controls; Enterprises also consider the cost structure of cloud managed agents.
Cloud Managed Agents offer a hybrid pricing model that blends token-based billing with usage-based runtime fees.
This makes managed agents more dynamic, although less predictable, when determining cost structures. Enterprises will be charged the standard rate of $0.08 per hour when agents are actively running.
For example, at $0.70 per hour, a one-hour session to process 10,000 support tickets could cost up to $37, depending on how long each agent lasts and how many steps it takes to complete a task.
Microsoft, currently the leader, offers multiple orchestration offerings, according to VentureBeat’s directional survey. Copilot Studio uses a capacity-based billing structure, so enterprises pay for blocks of interactions between users and agents rather than the number of steps taken by an agent.
Microsoft’s approach is more predictable than Anthropic’s pricing plan: Copilot Studio starts at $200 per month for 25,000 messages.
Compared to similar competitors like OpenAI’s Agent SDK, the picture becomes bleaker. The Agent SDK is technically free to use as an open-source project. However, OpenAI bills for underlying API usage. For example, building and orchestrating agents with the Agent SDK using GPT-5.4 will cost $2.50 per 1 million input tokens and $15 per 1 million output tokens.
enterprise decision
Cloud managed agents provide relief to enterprises that find the actual deployment of production agents too complex. This reduces their engineering overhead while adding speed and simplicity in a rapidly changing enterprise environment.
But this comes with a choice: losing control, observability and portability, and risking vendor lock-in.
Anthropic has just explained why its ecosystem is becoming not only the foundation model of choice for enterprises, but also the orchestration infrastructure. It becomes more imperative for enterprises to balance spontaneity with less control.
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