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The best cloud platform for building and running an AI agent depends on the stack you already use and the control you need. Amazon Bedrock AgentCore is the strongest fit when you want modular services that work with several agent frameworks and model providers. Google’s Gemini Enterprise Agent Platform suits teams that want low-code authoring alongside a code-first toolkit and an integrated managed runtime. Microsoft Foundry Agent Service is compelling for organizations built around Microsoft identity, enterprise tools, and a choice between configured prompt agents and hosted custom-code agents.
None is a defensible universal winner. The practical choice is the platform that can securely reach your data and tools, run your preferred framework and models, expose useful traces and evaluations, and fit your team’s operational skills. Treat the provider descriptions below as capability maps, not independent claims about speed, reliability, safety, or price.
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Which cloud platforms are serious options for AI agents?
Three current offerings cover the main patterns teams need: AWS Bedrock AgentCore, Google’s Gemini Enterprise Agent Platform, and Microsoft Foundry Agent Service. Each combines agent development with managed execution, identity, tool connectivity, and production controls, but they divide responsibilities differently.
- AWS Bedrock AgentCore: a modular set of services that can be adopted together or independently. AWS describes compatibility with open-source frameworks including CrewAI, LangGraph, LlamaIndex, and Strands Agents, plus multiple model providers.
- Gemini Enterprise Agent Platform: combines low-code Agent Studio with the open-source, model-agnostic Agent Development Kit (ADK). Google documents a managed runtime, sessions, Memory Bank, Agent Identity, Agent Gateway, a registry, and evaluation and observability features.
- Microsoft Foundry Agent Service: separates configured prompt agents from hosted agents. Prompt agents minimize code and runtime management; hosted agents let you supply framework-based or custom code while Microsoft manages hosting, endpoints, and scaling.
Product names and preview status can change. Confirm the current console labels, APIs, supported model and framework versions, and regional availability before committing an architecture.
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How do the three platforms compare?
The table is a starting point for a proof of concept, not a performance ranking. The capabilities are described by each provider and have not been independently benchmarked here.
| Platform | Development approach | Runtime and operations | Best evaluation angle |
|---|---|---|---|
| Amazon Bedrock AgentCore | Open-source frameworks such as CrewAI, LangGraph, LlamaIndex, and Strands Agents; multiple model providers; components usable together or independently | Managed runtime, memory, gateway and tool connectivity, identity, observability, and evaluation capabilities described by AWS | Framework and model choice, with separately selectable operational components |
| Gemini Enterprise Agent Platform | Agent Studio for low-code authoring; model-agnostic ADK for code-first workflows; access to more than 200 foundation models described by Google | Managed runtime, sessions, Memory Bank, Agent Identity, Agent Gateway, registry, and evaluation and observability capabilities | Integrated Google Cloud build, runtime, governance, and data services |
| Microsoft Foundry Agent Service | Configured prompt agents or hosted agents built with Agent Framework, LangGraph, OpenAI Agents SDK, Anthropic Agent SDK, GitHub Copilot SDK, or custom code | Managed endpoints and scaling, toolboxes, tracing and evaluation, Microsoft Entra identity, role-based access, and publishing integrations | Microsoft identity, enterprise tools, and the choice between configuration-driven and hosted custom logic |
What does Amazon Bedrock AgentCore provide?
AgentCore is designed as a collection of operational building blocks rather than a single all-or-nothing agent framework. You can select the services you need and keep an existing orchestration framework, while AWS supplies managed capabilities around execution and governance.
Development and model flexibility
A team can continue using a supported open-source framework such as CrewAI, LangGraph, LlamaIndex, or Strands Agents. AWS also describes support for multiple model providers, which is useful when a workflow requires different models for planning, extraction, coding, or cost control. Verify the exact framework releases, model combinations, and regions for your deployment; compatibility at the platform level does not guarantee that every version pairing is available everywhere.
Runtime, memory, and tools
AgentCore’s managed runtime is intended to remove routine infrastructure work. The service family also includes memory, gateway and tool connectivity, identity, observability, and evaluation capabilities. Because the pieces can be used independently, you can introduce one component—for example, a managed tool gateway—without immediately moving every part of an existing agent.
When AWS is a sensible shortlist candidate
- Your team has already standardized on one of the listed frameworks or wants to keep framework choice open.
- You expect to change model providers or use more than one provider in the same application.
- You want managed operational components but do not want a single low-code authoring environment to dictate the application design.
- You need to evaluate identity, tool policy, traces, and runtime behavior as separable architectural decisions.
Do not infer lower latency, lower cost, or better reliability from the modular design. Those outcomes depend on model selection, network paths, tool behavior, workload shape, and the controls you configure.
What does Gemini Enterprise Agent Platform provide?
Google’s platform combines a visual path for assembling agents with a code-first path for teams that need detailed orchestration. The current documentation uses the Gemini Enterprise Agent Platform name; older material may refer to Vertex AI Agent Builder or Agent Engine, so check the current product and API labels when following instructions.
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Agent Studio and the Agent Development Kit
Agent Studio provides low-code authoring for teams that need to assemble and iterate on an agent without building every runtime service themselves. For developers, the open-source Agent Development Kit is model-agnostic and supports code-first workflows. Google describes access to more than 200 foundation models through the platform, but the models available to a particular project, region, or API can differ.
Managed runtime and governance services
Google documents an Agent Runtime for managed execution, sessions for conversation state, Memory Bank for longer-lived memory, Agent Identity, Agent Gateway, a registry, and evaluation and observability capabilities. Together, these components address the path from an experiment to a catalogued, monitored service. Map each component to your data-residency, access-control, and retention requirements rather than assuming that a named feature automatically satisfies them.
What a first deployment requires
Google’s Agent Runtime quickstart requires a Google Cloud project with billing enabled and instructs you to enable the Agent Platform and Cloud Storage APIs. The deployment guidance also recommends cleaning up resources after experimenting to avoid continuing charges. Billing activation and API enablement are setup prerequisites, not evidence of a particular total cost.
What does Microsoft Foundry Agent Service provide?
Foundry’s defining decision is whether the service should run a configured prompt agent or host code that you supply.
Prompt agents
A prompt agent is configured through the service, so Microsoft manages the agent execution environment. This route is appropriate when instructions, model settings, and connected tools express the behavior you need and you want to minimize runtime and infrastructure management.
Hosted agents
A hosted agent contains your framework or custom code and runs on managed hosting with managed endpoints and scaling. Microsoft lists Agent Framework, LangGraph, the OpenAI Agents SDK, the Anthropic Agent SDK, the GitHub Copilot SDK, and custom code among hosted-agent choices. Hosted execution provides more control over orchestration, but your team still owns the correctness, dependency management, and security of that code.
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Identity, tools, and publishing
Microsoft describes toolboxes, tracing and evaluation, Microsoft Entra identity, role-based access, and publishing integrations. These are especially relevant when an agent must operate inside Microsoft-controlled identities or connect to enterprise services governed through existing roles. Validate the exact permissions, network boundaries, connector behavior, and publishing targets in the environment where the agent will run.
What should you compare before choosing a managed agent platform?
Use the following questions to turn a feature list into an architecture decision. The answer should be based on your representative workflow, not on the longest product page.
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Which framework and models must run?
Write down the exact orchestration framework, model families, tool libraries, and versions your prototype uses. Check each provider’s current support matrix and region restrictions. A platform that supports your framework in principle may still lack the model endpoint, runtime feature, or version combination you need.
How much infrastructure do you want to manage?
Compare a configured prompt agent, a managed runtime around your own code, and a fully custom deployment. Low-code and prompt-based approaches reduce operations but can constrain control. Hosted custom code preserves orchestration freedom while shifting responsibility for dependencies, testing, and application logic to your team. Modular services let you adopt only selected operational functions, but they require more architectural decisions.
Which tools and enterprise data must the agent reach?
Inventory every API, MCP server, search index, database, file store, and internal system the agent may call. For each connection, document authentication, authorization, network path, rate limits, data classification, and how tool definitions are updated. Compare gateway and toolbox behavior, not just the number of connectors advertised.
How will identity and least privilege work?
Define separate identities for the agent, its runtime, and sensitive tools where possible. Specify which actions require user context, which can run under a service identity, and which need approval. Then map those requirements to AgentCore identity controls, Google’s Agent Identity and gateway services, or Microsoft Entra identity and role-based access. Provider documentation describes the controls, but only your architecture review can establish whether they meet policy.
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Can operators diagnose failures?
Agents fail in ways ordinary request logs often miss: a planner can select the wrong tool, a tool can return malformed data, or a loop can consume tokens without progress. Confirm that traces show model calls, prompts or redacted equivalents, tool arguments and results, retries, latency, and termination reasons. Verify that evaluation features can replay representative tasks and that production monitoring alerts on the failure modes your users actually experience.
What will the workload cost in your region?
Model inference is only one line item. Include runtime, storage and memory, tool calls, network transfer where applicable, tracing, evaluation runs, and idle or retained resources. The material available for these platforms does not establish a current price winner or regional parity, so obtain live rates and model a realistic request mix before signing off.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you run a fair proof of concept?
A short, identical workflow on each shortlisted platform reveals trade-offs that capability lists cannot. Keep the prompts, tools, data sample, and success criteria fixed.
- Define the task and boundaries. Choose one production-shaped workflow, such as resolving a support case through an internal knowledge base and a ticketing API. Record allowed actions, prohibited actions, escalation conditions, and the data the agent may see.
- Implement the same tool contract. Expose equivalent API operations and schemas on every platform. Use test accounts and deterministic fixtures so a connector advantage does not masquerade as an agent-quality advantage.
- Use a fixed evaluation set. Include normal requests, ambiguous requests, missing data, permission failures, malformed tool responses, long conversations, and adversarial instructions. Score task completion, factuality, policy compliance, and correct escalation.
- Instrument operations. Capture end-to-end latency, model and tool timings, retries, token or request usage, trace completeness, and operator effort. Record every manual intervention needed to deploy, update, roll back, or investigate a run.
- Test identity and failure behavior. Attempt unauthorized tool calls, expired credentials, unavailable services, oversized inputs, and repeated model errors. Check whether the platform fails closed, surfaces a useful reason, and leaves an auditable record.
- Model ongoing cost. Apply current regional prices to the measured request mix, memory retention, runtime duration, evaluation frequency, and observability volume. Separate one-time setup from recurring usage.
- Make a reversible decision. Document which platform-specific services the agent depends on, exportable prompts and tool schemas, data portability, and the effort required to move the workflow if requirements change.
Which platform fits common team situations?
Choose AWS AgentCore first when framework freedom is the priority
Start with AgentCore when your developers already use CrewAI, LangGraph, LlamaIndex, or Strands Agents, or when changing model providers is a stated requirement. Its independently usable components can fit an incremental migration, provided the specific integrations and policies pass your review.
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Start with Gemini Enterprise Agent Platform when a visual authoring experience and a code-first ADK need to coexist, and when your data, identity, runtime, and evaluation services are already centered in Google Cloud. Confirm current naming, model access, API enablement, billing, and regional status before implementation.
Choose Foundry first when Microsoft identity and publishing dominate
Start with Microsoft Foundry Agent Service when Microsoft Entra permissions, enterprise tool access, or publishing into Microsoft environments are central constraints. Decide early whether a prompt agent is sufficient; move to a hosted agent when custom orchestration or framework code is essential.
What are the most common selection mistakes?
- Declaring a winner from feature counts: A longer list does not show that a feature works with your model, region, policy, or tool chain.
- Confusing a managed runtime with a managed agent: Hosting your code removes infrastructure tasks but does not remove responsibility for prompts, dependencies, permissions, or application errors.
- Ignoring tool governance: An agent’s risk is often determined by what it can do after a tool call, not by the model name. Scope permissions and log sensitive actions.
- Testing only happy paths: Include denied access, bad tool output, ambiguous intent, prompt injection, timeouts, and recovery. Otherwise you measure a demo, not an operational service.
- Using preview labels as permanent contracts: Recheck API versions, UI names, quotas, supported regions, and lifecycle commitments before production.
- Comparing sticker prices: Calculate inference, runtime, memory, tools, data movement, observability, and human review together. A low visible rate can be offset by operational work or a different request pattern.
What is the defensible recommendation?
Build a shortlist rather than choosing a universal champion. AgentCore is the modular, framework- and model-flexible route; Gemini Enterprise Agent Platform is the integrated low-code and code-first Google route; Foundry Agent Service is the Microsoft route with a clear prompt-agent versus hosted-code split. Run the same representative workflow on two or three candidates and select the one that meets your security, tool, observability, operational, and cost requirements with the least unacceptable compromise.
Frequently Asked Questions
Can I move an existing LangGraph agent between these platforms?
Possibly, because AWS, Google, and Microsoft each document support for LangGraph in some form. Portability still depends on model APIs, memory formats, tool authentication, tracing hooks, and provider-specific runtime code, so isolate those adapters and test a complete migration rather than assuming source-code compatibility.
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Do managed agent services eliminate the need for an application team?
No. They can manage hosting, scaling, identity integrations, or runtime services, but your team remains responsible for agent instructions, tool permissions, data handling, dependency updates, evaluation sets, and incident response. Hosted custom-code agents require especially careful ownership boundaries.
How should an agent handle sensitive internal data?
Classify the data before selecting connectors, then enforce least-privilege identities, explicit tool scopes, network controls, retention rules, and redacted observability. Verify how prompts, tool arguments, traces, memory, and evaluation records are stored and who can access them in the target region.
Is a low-code agent suitable for production workloads?
It can be, when the workflow fits the available configuration and tools and your evaluation demonstrates reliable behavior. Choose a hosted custom-code path when you need bespoke orchestration, complex state handling, specialized dependencies, or controls that the configured agent cannot express.
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