Anthropic’s Claude Managed Agents gives enterprises a new one-stop shop but raises vendor 'lock-in' risk
Our take
Anthropic announced a new platform last week, Claude Managed Agents, aiming to cut out the more complex parts of AI agent deployment for enterprises and competes with existing orchestration frameworks.
Claude Managed Agents is also an architectural shift: enterprises, already burdened with orchestrating an increasing number of agents, can now choose to embed the orchestration logic in the AI model layer.
While this comes with some potential advantages, such as speed (Anthropic proposes its customers can deploy agents in days instead of weeks or months), it also, of course, then also turns more control over the enterprise's AI agent deployments and operations to the model provider — in this case, Anthropic — potentially resulting in greater "lock in" for the enterprise customer, leaving them more subject to Anthropic's terms, conditions, and any subsequent platform changes.
But maybe that is 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 to a vendor-controlled runtime loop.
Even before the release of Claude Managed Agents, new directional VentureBeat research showed that Anthropic was gaining traction at the orchestration level as enterprises adopted its native tooling. Claude Managed Agents represents a new attempt by the firm to widen its footprint as the orchestration method of choice for organizations.
Anthropic is surging in orchestration interest
Orchestration has emerged as an important segment for enterprises to address 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 in February.
OpenAI closely followed at 25.7%. Both showed strong growth between the first two months of the year.
Anthropic, driven by increased interest in its offerings, such as Claude Code, over the past year, is putting up a fight.
Adoption of the Anthropic tool-use and workflows API increased from 0% to 5.7% between January and February. This tracks closely with the growing adoption of Anthropic’s foundation models, showing that enterprises using Claude turn to the company’s native orchestration tooling instead of adding a third-party framework.
While VentureBeat surveyed before the launch of Claude Managed Agents, we can extrapolate that the new tool will build on that growth, especially if it promises a more straightforward way to deploy agents.
Collapsing the external orchestration layer
Enterprises may find that a streamlined, internal harness for agents compelling, but it does mean giving up certain controls.
Session data is stored in a database managed by Anthropic, increasing the risk that enterprises become locked into a system run by a single company. This may be less desirable for some firms and compete with their desires to move away from the locked-in software-as-a-service (SaaS) applications in the current stacks, which many hope that AI will facilitate.
The specter of vendor lock-in means agent execution becomes more model-driven rather than direct by the organization, happens in an environment enterprises don’t fully control, and behavior becomes harder to guarantee.
It also opens the possibility of giving agents conflicting instructions, especially if the only way for users to exert any control over agents is to prompt them with more context.
Agents could have two control planes: one defined by the enterprises’ orchestration system through instructions and the other as an embedded skill from the Claude runtime.
This could pose an issue for highly sensitive and regulated workflows, such as financial analysis or customer-facing tasks.
Pricing, control and competitive set
Balancing control with ease is one thing; enterprises also consider the cost structure of Claude Managed Agents.
Claude Managed Agents introduces a hybrid pricing model that blends token-based billing with a usage-based runtime fee.
This makes Managed Agets more dynamic, though less predictable, when determining cost structures. Enterprises will be charged a standard rate of $0.08 per hour when agents are actively running.
For example, at $0.70 per hour, a one-hour session could cost up to $37 to process 10,000 support tickets, depending on how long each agent runs and how many steps it takes to complete a task.
Microsoft, currently the leader according to VentureBeat's directional survey, offers several orchestration offerings. 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 an agent takes.
Microsoft's approach tends to be 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 Agents SDK, the picture becomes murky. Agents SDK is technically free to use as an open-source project. However, OpenAI bills for the underlying API usage. Agents built and orchestration with Agents SDK using GPT-5.4, for example, will cost $2.50 per 1 million input tokens and $15 per 1 million output tokens.
The enterprise decision
Claude Managed Agents does give enterprises who find the actual deployment of production agents too complicated a reprieve. It reduces their engineering overhead while adding speed and simplicity in a fast-changing enterprise environment.
But that comes with a choice: lose control, observability and portability and risk further vendor lock-in.
Anthropic just made a case for why its ecosystem is becoming not just the foundation model of choice for enterprises, but also the orchestration infrastructure. It becomes more imperative for enterprises to balance ease with lesser control.
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- Anthropic wants to own your agent's memory, evals, and orchestration — and that should make enterprises nervousJust a few weeks after announcing Claude Managed Agents, Anthropic has updated the platform with three new capabilities that collapse infrastructure layers like memory, evaluation, and multi-agent orchestration, into a single runtime. This move could threaten the standalone tools that many enterprises cobble together. The new capabilities — 'Dreaming,' 'Outcomes,' and 'Multi-Agent Orchestration' — aim to make agents inside Claude Managed Agents “more capable at handling complex tasks with minimal steering,” Anthropic said in a press release. Dreaming deals with memory, where agents “reflect” on their many sessions and curate memories so they learns and surface unknown patterns. Outcomes allows teams to define and set specific rubrics to measure an agent's success, while Multi-Agent Orchestration breaks jobs down so a lead agent can delegate to other agents. Claude Managed Agents ideally provides enterprises with a simpler path to deploy agents and embeds orchestration logic in the model layer. It’s an end-to-end platform to manage state, execution graphs, and routing. With the addition of Dreaming, Outcomes and Multi-agent Orchestration, Claude Managed Agents expands capabilities even further and directly competes with tools like LangGraph or CrewAI, as well as external evaluation frameworks, RAG memory architectures, and QA loops. An integration threat Enterprises must now ask: Should we ditch our flexible, modular system in favor of an agent platform that brings almost everything in-house? Anthropic designed Claude Managed Agents to share context, state, and traceability in one place. This means the platform sees every decision agents make, rather than enterprises having to wire separate systems together. It sounds practical to have one platform that does everything. But not all enterprises want a full-service system. Claude Managed Agents already faces criticism that it encourages vendor lock-in because it owns most of the architecture and tools that govern agents. In the current paradigm, an organization may run Managed Agents but keep multi-agent orchestration, memory, or evaluations in a separate space ensures flexibility. The platform offers a fully-hosted runtime, which means memory and orchestration run on infrastructure the enterprise does not own. This can become a compliance nightmare for some organizations that have to prove data residency. Another problem to consider is that enterprises already in the middle of large-scale AI transformations must cobble together workarounds to deal with the constraints of their tech stack. Not every workflow is easily replaceable by switching to Claude Managed Agents. Dreaming and outcomes against current tools Most enterprises have a fragmented approach to AI deployment. For example, they may use LangGraph or Crew AI for agent routing and workflow management, Pinecone as a vector database for long-term memory, DeepEval for external evaluation, and a human-in-the-loop quality assurance to review some tasks. Anthropic hopes to do away with all of that. With Dreaming, Anthropic approaches memory by allowing users to actively rewrite it between sessions, so the agent essentially learns from its mistakes. Anthropic says this capability is useful for long-running states and orchestration. Current systems often handle memory persistence by storing embeddings, retrieving relevant context, and adding more state over time. Outcomes addresses the evaluation portion by detailing expectations for agents. Instead of external quality checks, which are often done by a team of humans, Anthropic is bringing evaluation into the orchestration layer rather than above it. But it’s the Multi-Agent Orchestration capability that pits Claude Managed Agents against orchestration frameworks from Microsoft, LangChain, CrewAI, and others. Model providers like Anthropic and OpenAI have already begun pushing aggressively into this space, arguing that bringing this to the model layer gives teams better control. Big decisions to make Enterprises face a big decision, and this one could depend on where they are in agent maturity. If an organization is still experimenting with agents and has not deployed many in production, they may find moving to Claude Managed Agents and configuring Dreaming and Outcomes to their needs much easier. This is the stage of development where, even if enterprises are using a third-party orchestrator like LangChain, they’re still customizing it. But for those who are already further along in the process, the calculation becomes trickier. It’s now a matter of parallel evaluation and better understanding of their processes. Businesses, though, will face the same decision even if they don’t intend to use Claude Managed Agents. Anthropic has signaled that other model and platform providers will likely shift their product roadmaps to a similar model that keeps everything locked in the same system — because models may become interchangeable, but the tooling and orchestration infrastructure will not.
- Anthropic Introduces Managed Agents to Simplify AI Agent DeploymentAnthropic introduces Managed Agents on Claude, a managed execution layer for agent-based workflows. It separates agent logic from runtime concerns like orchestration, sandboxing, state management, and credentials. The system supports long-running multi-step workflows with external tools, error recovery, and session continuity via a meta-harness architecture. By Leela Kumili
- Google and AWS split the AI agent stack between control and executionThe 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. “We want to provide a platform and a front door for companies to have access to all the AI systems and tools that Google provides,” Maryam Gholami, senior director, product management for Gemini Enterprise, told VentureBeat in an interview. “The way you can think about it is that the Gemini Enterprise Application is built on top of the Gemini Enterprise Agent Platform, and the security and governance tools are all provided for free as part of Gemini Enterprise Application subscription.” On the other hand, AWS added a new managed agent harness to Bedrock Agentcore. The company said in a press release shared with VentureBeat 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. Data sources change, or tools can return conflicting responses. But the agent becomes more vulnerable to inconsistencies and becomes less truthful. Agent reliability becomes a systems problem, and managing that drift may need more than faster execution; it may require visibility and control. It’s this failure point that platforms like Gemini Enterprise and AgentCore try to prevent. Though this shift is already happening, Gholami admitted that customers will dictate how they want to run and control any long-running agent. “We are going to learn a lot from customers where they would be using long-running agents, where they just assign a task to these autonomous agents to just go ahead and do,” Gholami said. “Of course, there are tricks and balances to get right and the agent may come back and ask for more input.” The new AI stack What’s becoming increasingly clear is that the AI stack is separating into distinct layers, solving different problems. AWS and, to a certain extent, Anthropic and OpenAI, optimize for faster deployment. Claude Managed Agents abstracts much of the backend work for standing up an agent, while the Agents SDK now includes support for sandboxes and a ready-made harness. These approaches aim to lower the barrier to getting agents up and running. Google offers a centralized control panel to manage identity, enforce policies and monitor long-running behaviors. Enterprises likely need both. As some practitioners see it, their businesses have to have a serious conversation on how much risk they are willing to take. “The main takeaway for enterprise technology leaders considering these technologies at the moment may be formulated this way: while the agent harness vs. runtime question is often perceived as build vs. buy, this is primarily a matter of risk management. If you can afford to run your agents through a third-party runtime because they do not affect your revenue streams, that is okay. On the contrary, in the context of more critical processes, the latter option will be the only one to consider from a business perspective,” Rafael Sarim Oezdemir, head of growth at EZContacts, told VentureBeat in an email. Iterating quickly lets teams experiment and discover what agents can do, while centralized control adds a layer of trust. What enterprises need is to ensure they are not locked into systems designed purely for a single way of executing agents.
- Kimi K2.6 runs agents for days — and exposes the limits of enterprise orchestrationMost orchestration frameworks were built for agents that run for seconds or minutes. Now that agents are running for hours — and in some cases days — those frameworks are starting to crack. Several model providers, such as Anthropic with Claude Code and OpenAI with Codex, introduced early support for long-horizon agents through multi-session tasks, subagents and background execution. However, these systems sometimes assume agents are still operating within bounded-time workflows even when they run for extended periods. Open-source model provider Moonshot AI wants to push beyond that with its new model, Kimi K2.6. Moonshot says the model is designed for continuous execution, with internal use cases including agents that ran for hours and, in one case, five straight days, handling monitoring and incident response autonomously. But this growing use of this type of agent is exposing a critical gap in orchestration: most orchestration frameworks were not designed for this type of continuous, stateful execution. Open-source models, such as Kimi K2.6, that rely on agent swarms are making the case that their orchestration approach comes close to managing stateful agents. The difficulties of orchestrating long-running agents While it is true that some enterprises would rather bring their own orchestration frameworks to their agentic ecosystem, model providers and agent platforms recognize that offering agent management remains a competitive advantage. Other model providers have begun exploring long-running agents, many through multi-session tasks and background execution. For example, Anthropic’s Claude Code orchestrates agents with a lead agent that directs other agents based on a set of user-instructed definitions. OpenAI’s Codex runs similarly. Kimi K2.6 approaches orchestration with an improved version of its Agent Swarms, capable of managing up to 300 sub-agents “executing across 4,000 coordinated steps simultaneously,” Moonshot AI wrote in a blog post. Compared to both Claude Code and Codex, K2.6 relies on the model, rather than pre-defined roles, to determine orchestration. Kimi K2.6 is now available on Hugging Face, through its API, Kimi Code and the Kimi app. Practitioners experimenting with long-horizon agents say the brittleness runs deeper than prompting can fix. As one practitioner, Maxim Saplin, put it in a blog post, “That does not mean subagents are useless. It means orchestration is still fragile. Right now, it feels more like a product and training problem than something you can solve by writing a sufficiently stern prompt.” The problem long-running agents pose is that it’s difficult to maintain their state, especially as their environment continues to change while they're doing their job. The agent would constantly call different tools and APIs or tap into different databases during its runtime. Most current agents, those that may run for one or two executions, do call different tools, but for at most a minute. Mark Lambert, chief product officer at ArmorCode, which builds an autonomous security platform for enterprises, told VentureBeat in an email that the governance gap is already outpacing deployment. "These agentic systems can now generate code and system changes faster than most organizations can review, remediate, or govern them. This will require more than just additional scanning. Organizations will need stronger AI governance that provides the context, prioritization, and accountability teams need to manage Kimi and other AI-generated risk before they turn into accumulated exposure," Lambert said. Long-running agents could also risk failure without a clear rollback. Most importantly, these types of agents often lack a set of well-defined tasks and dynamically adjust their plans as they run. Kunal Anand, chief product officer at F5, told VentureBeat in an email that long-horizon agents represent a much bigger architectural shift than most companies were prepared for. “We went from scripts to services to containers to functions, and now to agents as persistent infrastructure. That creates categories we do not yet have good names for: agent runtime, agent gateway, agent identity provider, agent mesh. The API gateway pattern is morphing into something that has to understand goals and workflows, not just endpoints and verbs,” Anand said. Running for 13 hours and even five days Understanding how to orchestrate agents becomes important because model capabilities have begun to outpace orchestration innovations, even as enterprises start to look at long-horizon agents. Moonshot AI says the model is built for tasks that reflect "real-world challenges that typically demand weeks or months of collective human effort." In a separate technical document provided to VentureBeat, Moonshot claims K2.6 built a full SysY compiler from scratch in 10 hours — work it characterized as equivalent to a team of four engineers over two months — and passed all 140 functional tests without human intervention. The team deployed K2.6 to complex engineering tasks, including overhauling an eight-year-old open source financial matching engine. Moonshot's engineers described a 13-hour execution that “iterated through 12 optimization strategies, initiating over 1,000 tool calls to modify more than 4,000 lines of code precisely.” Moonshot said one of its teams used K2.6 to build an agent that ran autonomously for five days. That agent managed monitoring, incident response and system operations.