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There is no universal winner among the leading cloud platforms for building and running AI agents. AWS Bedrock AgentCore is strongest when you want modular services that work with several frameworks and model providers. Google’s Gemini Enterprise Agent Platform combines low-code authoring, a code-first development kit, and a managed lifecycle. Microsoft Foundry Agent Service offers configuration-driven prompt agents and hosted custom-code agents with Microsoft identity and enterprise integrations. Choose by matching your existing cloud, agent framework, models, tools, security controls, data connections, and operations team—not by assuming one provider is faster, cheaper, or more reliable. The practical way to decide is to shortlist platforms, run the same representative workflow on each, and measure success, latency, cost, tool failures, trace quality, and operational effort.
Is there one best cloud platform for AI agents?
No. The best choice depends on how much code and infrastructure your team wants to own, which models and frameworks you need, and where your data and identities already live. The three platforms covered here are provider-described products, not independent performance benchmarks.
Use this quick orientation:
- Choose AWS Bedrock AgentCore when you need separately selectable runtime, memory, gateway, identity, observability, and evaluation services, and you want to keep using supported open-source frameworks or multiple model providers.
- Choose Gemini Enterprise Agent Platform when Google Cloud is your operating environment and you want both a low-code path through Agent Studio and a code-first path through the model-agnostic Agent Development Kit (ADK).
- Choose Microsoft Foundry Agent Service when Microsoft Entra identity, Microsoft tools, Teams or Microsoft 365 publishing, and a clear choice between managed prompt agents and hosted custom code are central requirements.
Product names, supported models, APIs, preview labels, and regional coverage can change. Confirm the current documentation for the exact edition and region before committing.
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What does a cloud AI-agent platform actually provide?
A platform is more than a model endpoint. It supplies some combination of an execution runtime, conversation or task state, tool and data connections, identity, deployment controls, tracing, evaluation, and scaling. Those layers determine how an agent behaves in production after the prototype works.
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Model and framework access
Your agent may use a single foundation model, route among several models, or combine a model with deterministic code and retrieval. Framework support matters because orchestration semantics, tool definitions, streaming, and state handling differ between frameworks. Verify supported versions rather than relying on a framework name alone.
Runtime and state
The runtime starts agent turns, handles sessions, and provides an endpoint for applications. Memory or state services preserve information between turns or tasks. Ask whether state is transient, durable, tenant-isolated, region-bound, encrypted, and removable on request.
Tools, data, and identity
Production agents call APIs, search indexes, databases, files, and business systems. A gateway or toolbox can centralize those calls, but the important questions are authentication, authorization, network reachability, audit logs, rate limits, and how quickly a revoked tool permission takes effect.
Operations
Tracing should show the model request, retrieval steps, tool calls, inputs and outputs, latency, and failures without exposing secrets. Evaluation services help you compare versions against representative tasks. Managed scaling can reduce infrastructure work, but it does not remove the need for budgets, quotas, incident response, and data governance.
How do AWS, Google Cloud, and Microsoft compare?
The table separates documented product approaches from conclusions you should validate in a proof of concept.
| Platform | Development approach | Runtime and operations | Best evaluation angle |
|---|---|---|---|
| Amazon Bedrock AgentCore | Works with open-source frameworks such as CrewAI, LangGraph, LlamaIndex, and Strands Agents, and with multiple model providers. Components can be used together or independently. | AWS describes managed runtime, memory, gateway and tool connectivity, identity, observability, and evaluation capabilities. | Assess it when framework and model choice, plus independently selectable operational components, matter. Verify each required service, integration, policy, and region. |
| Gemini Enterprise Agent Platform | Agent Studio provides low-code authoring. The open-source, model-agnostic Agent Development Kit supports code-first workflows. Google describes access to more than 200 foundation models through the platform. | Google documents managed runtime, sessions, Memory Bank, Agent Identity, Agent Gateway, a registry, and evaluation and observability features. | Assess it when Google Cloud’s integrated build, runtime, governance, and data services fit your environment. Confirm product names and preview status for your deployment. |
| Microsoft Foundry Agent Service | Configured prompt agents are managed by the service. Hosted agents run framework-based or custom code; documented choices include Agent Framework, LangGraph, OpenAI Agents SDK, Anthropic Agent SDK, GitHub Copilot SDK, and custom code. | Microsoft describes managed endpoints and scaling, toolboxes, tracing and evaluation, Microsoft Entra identity, role-based access, and publishing integrations. | Assess it when Microsoft identity, enterprise tools, or Teams and Microsoft 365 publishing are important. Decide whether configuration-driven execution or hosted custom code fits. |
What should I look for in a managed AI-agent platform?
Score each candidate against the same workload rather than counting feature names. The following six axes expose differences that a marketing checklist can hide.
1. Framework and model flexibility
Write down the exact framework, model families, embedding model, reranker, and safety components your design requires. Then verify supported versions, invocation modes, streaming behavior, tool-calling support, and regional availability. AWS emphasizes multiple frameworks and model providers; Google’s ADK is model-agnostic; Microsoft lists several hosted-agent frameworks. None of those statements guarantees support for every version or combination.
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Decide which layer your team wants to operate. A configured prompt agent minimizes runtime code and deployment work. A hosted custom-code agent gives you control over orchestration, dependencies, retries, and business logic while the provider manages hosting concerns. A modular service set lets you adopt only the components you need, but it also creates more architecture and policy decisions.
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3. Tool and enterprise-data access
Create an inventory of every API, MCP server, search system, database, file store, and queue the agent will use. For each one, record the credential type, network path, timeout, write permissions, data classification, and owner. Compare how each platform registers tools, updates schemas, handles failures, and prevents an agent from calling an operation outside its allowed scope.
4. Identity and security
Map the agent’s identity to least-privilege roles, not to a developer’s broad credentials. Check workload identity, user delegation, secret storage, private networking, encryption, tenant isolation, policy enforcement, and audit retention. AWS’s Agentic AI Lens, Google’s identity and gateway components, and Microsoft Entra and role-based access controls all address parts of this problem; your architecture still has to define the actual permissions.
5. Observability and evaluation
List the failures you must diagnose: incorrect tool selection, malformed arguments, retrieval misses, unsafe outputs, long model waits, retries, and partial writes. Confirm that traces expose those events and that evaluation can run repeatable test cases with your data and tools. A platform that records only final text will not explain why an agent failed.
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6. Cost and geography
Model costs for inference, runtime, storage or memory, tool calls, networking, logs, and evaluation. The material available for this comparison does not establish a current price winner or regional parity, so do not rank the platforms on cost. Confirm service availability, quotas, data residency, and billing-account requirements in every target region.
Why choose Amazon Bedrock AgentCore?
AgentCore is a good fit for teams that want a modular operating layer around an agent they have already designed in a preferred framework or with a preferred model provider.
Development model
AWS describes compatibility with CrewAI, LangGraph, LlamaIndex, and Strands Agents, along with multiple model providers. That makes the platform attractive when replacing an orchestration framework or model is a realistic future requirement. Confirm the exact adapter, version, streaming mode, and tool-calling behavior for your chosen combination.
Operational components
AgentCore components can be used together or independently. The documented set includes a managed runtime, memory, gateway and tool connectivity, identity, observability, and evaluation. You can therefore adopt a runtime without immediately moving every state or tool concern into the same service, but independent components require deliberate integration and access policies.
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- Which AgentCore components are available in every region where the agent will run?
- Does the selected framework preserve streaming, callbacks, retries, and custom middleware?
- How are model-provider credentials and tool credentials separated?
- Which trace fields are retained, redacted, and exportable to your incident system?
AWS’s Agentic AI Lens is useful for reviewing identity, reliability, security, and operational design, but it is guidance rather than an independent benchmark of AgentCore.
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Why choose Gemini Enterprise Agent Platform?
Google’s platform suits teams that want a single Google Cloud path from authoring through managed runtime, governance, and data connections, with a choice between visual and code-first development.
Two authoring paths
Agent Studio is the low-code option for assembling an agent without building every orchestration detail from scratch. The open-source Agent Development Kit is the code-first option and is described as model-agnostic. A team can prototype visually, then move selected workflows into code, provided it verifies feature parity and migration effort.
Managed lifecycle services
Google documents Agent Runtime for execution, sessions for conversation state, Memory Bank for longer-lived memory, Agent Identity, Agent Gateway, a registry, and evaluation and observability capabilities. These names describe separate responsibilities; check the current API surface and release status before designing around one.
Model breadth and verification
Google describes access to more than 200 foundation models through the platform. Treat that as a platform-level availability statement, not a promise that every model is enabled in every region, supports every tool mode, or has identical quotas. Test the specific model and fallback sequence your agent will use.
Deployment prerequisite
Google’s Agent Runtime quickstart requires a Google Cloud project with billing enabled and enables the Agent Platform and Cloud Storage APIs. Follow the documented cleanup procedure after experiments; leaving resources enabled can create charges. This is a setup requirement, not a total-cost estimate.
Why choose Microsoft Foundry Agent Service?
Foundry Agent Service is compelling when Microsoft identity, access control, enterprise tooling, or publishing into Microsoft collaboration products is part of the target architecture.
Prompt agents versus hosted agents
A prompt agent is configured rather than supplied as a full application. The service manages its execution, which reduces code and runtime management for straightforward instructions, model settings, and tools. A hosted agent is customer-supplied code running on managed hosting. It is the better match when you need custom orchestration, dependencies, state handling, or framework behavior that configuration cannot express.
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Hosted framework choices
Microsoft’s documentation lists Agent Framework, LangGraph, OpenAI Agents SDK, Anthropic Agent SDK, GitHub Copilot SDK, and custom code among hosted-agent options. Verify the current support level, deployment packaging, networking model, and upgrade process for the framework you intend to use.
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Which cloud should I use to deploy an AI agent?
Start with the constraints that are expensive to change, then use features to break ties.
- Identify the system of record. Choose the cloud where the agent’s private data, identity directory, networking, and operational team already exist unless a strong requirement argues otherwise.
- Freeze the agent contract. Document inputs, outputs, tools, model calls, memory, latency targets, human approvals, and failure behavior. This prevents a platform demo from substituting for a production design.
- Shortlist two or three platforms. Include the incumbent cloud and any platform that uniquely supports your framework, model, identity, or publishing requirement.
- Map every integration. For each tool and data source, record authentication, network route, permissions, schema updates, rate limits, and ownership. Mark any item that requires a custom connector.
- Choose the operating model. Decide whether a configured prompt or low-code agent is sufficient, whether hosted custom code is necessary, or whether modular services are preferable.
- Validate governance. Confirm region, retention, encryption, audit, approval, incident response, and deletion requirements with the security and legal owners.
- Run a representative proof of concept. Use production-like documents, tools, permissions, and failure cases. Record measurements rather than relying on a successful happy-path demo.
How should I run a fair platform proof of concept?
Use the same agent objective and acceptance tests on every candidate. Keep prompts, tool schemas, data snapshots, and model settings as equivalent as the platforms allow.
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- Define success before testing. Specify required facts, allowed actions, citation or evidence rules, maximum latency, and when the agent must ask for human approval.
- Instrument each run. Capture end-to-end latency, model latency, token usage where available, tool-call count, retries, errors, memory reads and writes, and trace completeness.
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- Test security boundaries. Attempt cross-tenant access, unauthorized tools, prompt injection through retrieved content, secret exposure, and replay of an old credential.
- Model the bill. Apply current provider rates to the measured call pattern, including idle runtime, storage, logs, networking, and evaluation. Keep promotional or one-time credits separate from recurring estimates.
- Review portability. List code, prompts, tool definitions, state formats, and telemetry that would have to change if you moved providers.
The result should be a decision record with measured workload evidence and unresolved risks, not a generic platform ranking.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What security and governance controls are essential?
An agent should receive only the permissions required for the current task, and every consequential action should be attributable to an identity and trace.
- Separate identities. Distinguish the end user, the agent workload, and each downstream tool. Do not place long-lived administrator keys in prompts, environment variables, or source repositories.
- Constrain tools. Expose narrow operations instead of unrestricted database or shell access. Validate arguments, enforce server-side authorization, and require confirmation for irreversible actions.
- Control retrieved content. Treat documents and web pages as untrusted input. Filter instructions embedded in content and keep retrieval permissions aligned with the requesting user.
- Protect traces. Redact secrets and regulated data, define retention, and restrict who can inspect prompts, tool arguments, and outputs.
- Plan human escalation. Specify when the agent pauses, what evidence it supplies, and how an operator resumes or cancels the task.
- Version everything. Keep prompts, tools, policies, model selections, evaluation sets, and infrastructure definitions under change control.
Each provider documents relevant identity, gateway, role, and observability features, but the platform does not automatically create a compliant architecture. Your security design determines the effective control.
What are the most common implementation mistakes?
Choosing from a feature list
A long list of runtimes, memories, and model names does not show that your workflow will succeed. Replace feature counting with the representative proof of concept described above.
Assuming framework names mean identical behavior
The same framework can behave differently around streaming, retries, callbacks, sandboxing, and tool schemas after deployment. Run framework-specific tests on the target platform.
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Ignoring regional and preview status
Documentation can describe a capability that is limited to selected regions or a preview release. Check the service region, API version, quota, support policy, and data-residency impact immediately before launch.
Underestimating tool failure handling
Design explicit timeouts, idempotency keys, retries with limits, compensating actions, and human review. An agent that can call a tool but cannot recover from a partial write is not production-ready.
Leaving experimental resources running
Disable test runtimes, storage, logs, and endpoints after a trial. Google’s quickstart specifically calls out cleanup to avoid ongoing charges; apply the same discipline on every cloud.
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A provider can scale an endpoint and record traces without guaranteeing factual answers, safe actions, or business-level uptime. Those outcomes require your prompts, tools, evaluations, permissions, and incident procedures.
How can I reduce lock-in without giving up managed services?
Keep business rules and tool contracts in your application layer while using provider services for the parts that deliver clear operational value.
- Define tool schemas and error codes independently of a provider’s console.
- Store prompts, evaluation cases, and policy tests in version control.
- Use adapters for model invocation and tracing so a model or telemetry backend can change without rewriting orchestration.
- Export important traces, state records, and audit events in a documented format.
- Document provider-specific features—such as memory, gateways, or publishing integrations—as replaceable dependencies with a fallback plan.
Portability has a cost. Duplicating every managed feature can increase engineering and operations work, so make the trade-off explicit rather than pursuing abstract multi-cloud symmetry.
What is the practical recommendation?
Shortlist the platform that matches your existing identity, data, framework, and operations model, then prove it with the same difficult workflow on at least one alternative. AWS Bedrock AgentCore is the natural first evaluation for modular, framework- and model-flexible designs. Gemini Enterprise Agent Platform is a strong first evaluation for Google Cloud teams wanting low-code and code-first paths with an integrated managed lifecycle. Microsoft Foundry Agent Service is a strong first evaluation for Microsoft-centric environments that need prompt agents, hosted custom code, Entra identity, and enterprise publishing. Treat all three as evolving products and validate the exact services, regions, APIs, and policies your launch requires.
Frequently Asked Questions
Can I move an AI agent from one cloud to another later?
Usually, but the effort depends on how much provider-specific state, identity, tool wiring, and telemetry the agent uses. Keep tool contracts, prompts, evaluation cases, and business rules portable, and document every managed dependency so a migration is an engineering project rather than a rewrite from scratch.
When should I use a prompt or low-code agent instead of hosted code?
Use the configuration-driven path when instructions, model settings, and a bounded tool set meet the requirement and your team wants minimal runtime maintenance. Move to hosted code when you need custom orchestration, dependency management, specialized state handling, framework middleware, or recovery logic that configuration cannot express.
Do these platforms guarantee accurate or safe agent behavior?
No. Provider documentation describes capabilities such as runtimes, identity, tracing, and evaluation, not a guarantee of factual accuracy or safe business actions. Accuracy and safety depend on your models, retrieval data, tool permissions, tests, human approvals, and monitoring.
How do I check whether a feature is available in my region?
Open the provider’s current service documentation for the target region and verify the API version, release status, quota, model list, data-residency rules, and networking options. Repeat the check immediately before launch because agent-platform labels and regional availability change quickly.
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