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For teams choosing where to run an AI agent, five offerings have enough official documentation for a useful comparison: AWS Bedrock AgentCore Runtime, Google Gemini Enterprise Agent Runtime, Microsoft Foundry Agent Service, Cloudflare Agents, and Anthropic Claude Managed Agents. The available evidence does not establish a definitive set of eight, so this guide does not pad the comparison with frameworks or model APIs.
The key distinction is what the service actually operates. A model API supplies inference; an agent framework or SDK helps define behavior; a managed runtime hosts agent execution and may also manage tools, state, or operations. Those categories can overlap, but they are not interchangeable.
What counts as a managed agent runtime?
A managed runtime takes responsibility for executing an agent remotely, rather than simply giving developers a model endpoint or code library. The service may also provide deployment, scaling, identity, state, tools, or observability—but the scope varies by provider and product path.
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Five documented options
AWS Bedrock AgentCore Runtime
AWS describes AgentCore Runtime as a managed environment for deploying and running agents or tools. Its documentation lists framework support that includes LangGraph, Strands, CrewAI, and OpenAI Agents SDK, and says agents can use models inside or outside Bedrock. These are vendor-stated compatibility claims; confirm that the specific framework and model work on the deployment path you plan to use.
AWS documents two compute choices: serverless microVMs and Instances running on AWS-managed EC2 infrastructure in the customer’s account. Its FAQ, checked October 7, 2026, states maximum asynchronous-work durations of 8 hours on microVMs and 14 days on Instances. These are service limits, not performance benchmarks.
Microsoft Foundry Agent Service
Microsoft describes prompt-agent and hosted-agent paths. Hosted agents can use Agent Framework, LangGraph, OpenAI Agents SDK, Anthropic Agent SDK, GitHub Copilot SDK, or custom code; Microsoft says developers can provide a container image or source ZIP. The platform description also includes managed endpoints, automatic scaling, dedicated Entra identity, session-level state persistence, end-to-end observability, managed toolboxes, model catalog access, and publishing or sharing options.
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Those capabilities make the hosted-agent path worth examining when you want managed deployment and operational services alongside your agent code. Verify the specific controls and deployment requirements for the service configuration you intend to use.
Google Gemini Enterprise Agent Runtime
Google’s current documentation uses the Gemini Enterprise Agent Platform and Agent Runtime names. The API resource remains ReasoningEngine for backward compatibility. Documented deployment templates and paths include LangGraph, LangChain, AG2, and LlamaIndex.
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Evaluate Agent Runtime in the context of Google Cloud’s broader Agent Platform, and check current terms, availability, and pricing for the exact feature you need. The framework list alone does not establish that every feature behaves identically across deployment paths.
Cloudflare Agents
Cloudflare describes Agents as a runtime for agent state, communication, execution, and operations. Its model-use documentation says agents can connect to OpenAI, Anthropic, Google Gemini, or any service with an OpenAI-compatible API; it also points to AI Gateway for routing and related controls.
This documents model connectivity and runtime functions, but it does not establish parity with other providers’ lifecycle management or enterprise capabilities. Assess the actual state, security, and operational controls you need rather than treating model connectivity as proof of full portability.
Anthropic Claude Managed Agents
Anthropic’s April 8, 2026 announcement described Claude Managed Agents as a composable API suite for building and deploying agents, with native MCP, tool integrations, memory, and infrastructure. The announcement described the service as public beta on that date. Check Anthropic’s current documentation for availability, limits, and terms before making a deployment decision.
Compare the responsibilities, not just the names
Use the same questions for each product and for the specific deployment path under consideration. Vendor compatibility statements are not guarantees of complete portability, and a provider’s feature description is not an independent evaluation.
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- Hosting: Does the service run the agent loop, only execute a container or tool, or just provide model inference?
- Deployment: Can you deploy through an SDK or API, upload source, provide a container, use serverless execution, or run compute in your own account?
- Runtime behavior: What are the maximum execution duration, background-work, streaming, concurrency, cold-start, and session-isolation characteristics?
- Frameworks and models: Which combinations are supported on the exact path you plan to use? Are features tied to a particular model, harness, or cloud?
- State and memory: What session or conversation state is persisted, who controls retention, and how can it be exported or deleted?
- Tools: Which tools are built in? Is MCP supported? How are custom tools, credentials, and approvals handled?
- Security and networking: What identity, isolation, secret-handling, and private-network controls are available?
- Operations: Which logs, traces, metrics, evaluations, deployment/version controls, and incident workflows are included?
- Availability and cost: Is the relevant feature generally available or in preview in your region? Which model, tool, and compute charges apply?
- Exit options: Can you move agent code, state, tool configuration, and observability data elsewhere?
Choose by workload and operating model
Start with execution duration
If an agent must perform asynchronous work for hours or days, compare published duration limits for the exact compute option. AWS’s documented 8-hour microVM and 14-day Instances limits are specific to those AWS options; they should not be generalized to the other services.
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Match the runtime to your existing cloud and controls
Consider where your identity, network boundaries, secrets, monitoring, and deployment workflows already live. A service’s managed features are valuable only if they fit your security requirements and operating model. Check whether the provider-hosted path gives your team the level of control it needs over the agent loop and tools.
Test portability on a real deployment path
A framework or model appearing on a compatibility list is not proof that state, tools, observability, or deployment settings transfer cleanly. Validate the precise framework-model combination and the features your agent uses, including how you would move its data and configuration if you changed runtimes.
What this comparison can—and cannot—establish
The five products above are documented options, not an exhaustive eight-product lineup or a ranked list. Product descriptions come from their providers, and no independent cross-runtime benchmark is established here for latency, uptime, cost savings, adoption, or market share. The right choice depends on your cloud footprint, workload duration, framework and model needs, security boundaries, and desired control over the agent loop.
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Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
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