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There is no universal winner. Oracle Cloud Infrastructure (OCI) gives you an API-first Responses API for managed agent orchestration plus a separate service for hosting containerized, customer-written runtimes. AWS now directs new customers away from Amazon Bedrock Agents Classic toward Amazon Bedrock AgentCore, which offers both a managed harness and a code-based runtime. Microsoft Foundry Agent Service covers managed prompt agents, managed voice prompt agents, and hosted agents that run your code.
Choose among them by deciding how much runtime code your team will own, which models and tools are available in your target region, where state and data must reside, how identities and networks are controlled, and what your complete operating cost will be. The published product information does not establish a cross-cloud winner for price, latency, quality, or security.
What the three platforms actually provide
The phrase “AI agent service” hides three different abstractions. A managed agent service can execute the reasoning loop, call approved tools, retain conversation state, and expose an endpoint without your team implementing the runtime. A hosted-agent service instead runs a runtime that your team packages and maintains. An API-first service supplies primitives that your application orchestrates. These choices affect control, portability, operational work, and failure modes more than the cloud brand does.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstall| Provider | Current service | Primary abstraction | Customer-code hosting | Documented capabilities | Lifecycle note |
|---|---|---|---|---|---|
| Oracle Cloud Infrastructure | OCI Responses API in OCI Generative AI, plus OCI Generative AI Applications | API-managed interaction and orchestration; separate managed application hosting | Yes. Containerized custom agent applications with networking, scaling, storage and authentication settings | File Search, Code Interpreter, Function Calling, MCP Calling, conversation context, long-term memory and short-term context compaction | Enterprise AI Agents was announced generally available on March 31, 2026; verify region and model support for each capability |
| AWS | Amazon Bedrock AgentCore; Amazon Bedrock Agents Classic remains for existing customers | Configuration-based managed harness or code-based runtime deployed to AgentCore Runtime | Yes. Deploy orchestration code built with supported frameworks | Managed loop, framework runtimes, optional memory, gateway, credentials, evaluators, logs and traces | AWS says Bedrock Agents Classic is no longer open to new customers and points new evaluations to AgentCore |
| Microsoft Azure | Microsoft Foundry Agent Service | Managed prompt and voice prompt agents, or hosted agents running customer code | Yes. Supply a container image or source ZIP that Foundry builds into an image | Model catalog, web and file search, code interpreter, memory, custom functions, OpenAPI tools, MCP servers, reusable toolboxes, tracing, evaluation and publishing | Voice-related and some monitoring, evaluation, avatar, WebRTC and telephony capabilities are preview features; check current terms |
Start with the service names in this table when designing a new project. Older articles that present “Bedrock Agents” as AWS’s default path do not reflect the current new-customer guidance.
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Oracle Cloud Infrastructure: API orchestration plus managed runtime hosting
OCI Responses API for managed agent workflows
OCI documents the OCI Responses API as an API-first, OpenAI-compatible interface. OCI manages model interaction and the execution context while your application sends requests and handles the surrounding business workflow. The documented surface includes conversation state, tools and supporting resources, so a team can keep its user interface and business APIs while delegating the agent loop to OCI.
- File Search supports retrieval over files attached to an agent workflow.
- Code Interpreter executes Python in a sandbox with no external network access. Oracle’s building guide lists more than 420 preinstalled Python libraries; this is a documented product figure, not a cross-provider benchmark.
- Function Calling lets the model request application-defined operations that your permission layer must approve and execute.
- MCP Calling connects to Model Context Protocol servers where the network and identity configuration permits it.
- Memory controls include conversation context, long-term memory and short-term context compaction. Confirm retention and isolation settings for the exact tenancy and resource configuration.
Because the interface is API-first, you can place your own authentication, user-session policy, retries, business rules and observability around the call. That flexibility also means your team remains responsible for application-level orchestration decisions that a fully managed agent product might hide.
OCI Generative AI Applications for customer-written runtimes
When the agent is implemented in a framework or service you control, OCI Generative AI Applications provide managed hosting for a containerized runtime. Oracle’s building guidance describes configuring networking, scaling, storage and authentication. This is a deployment choice rather than a different model: your code owns the loop, tool adapters and framework behavior, while OCI operates the hosting layer.
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Use this path when the agent must run custom middleware, nonstandard control flow, proprietary planning logic or libraries that do not fit a declarative managed agent. You still need to design health checks, dependency updates, secrets handling, authorization for each tool, and safe shutdown and retry behavior in the application.
Hybrid OCI architecture
Oracle documents a hybrid pattern in which a hosted custom runtime calls the Responses API for model interaction or selected managed capabilities. This lets you retain code-level control over routing and business logic while using OCI-managed conversation resources and tools where they fit. Define the boundary explicitly: decide which component owns state, which identity is used for each tool call, and how traces correlate across the hosted application and API requests.
Data services, projects and regional scope
Oracle’s March 31, 2026 general-availability announcement describes managed vector stores for retrieval-augmented generation and NL2SQL, projects that organize and isolate agent resources, managed application hosting with public and private endpoint support, and database queries under permission controls without moving or copying database content. These features can reduce data movement, but database permissions still need to be scoped to the agent’s actual tasks.
The announcement lists Chicago, Ashburn, Phoenix, Frankfurt, London, Osaka, Hyderabad, São Paulo and Riyadh. Treat that list as a starting point, not a guarantee: model, tool and endpoint availability can differ by region and can change after publication. Check the current OCI service and model matrix before committing a production topology.
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AWS: use AgentCore for new evaluations
Why Amazon Bedrock Agents Classic changes the comparison
The current Amazon Bedrock Agents documentation labels the older service “Amazon Bedrock Agents Classic” and states that it is no longer open to new customers. Existing customers may continue using it. A new AWS project should therefore evaluate AgentCore rather than assume Classic is an available starting point.
AgentCore managed harness
The AgentCore quickstart describes a configuration-based harness in which AgentCore runs the agent loop. This is the closest AWS option to a managed orchestration service: you specify the agent configuration and connected components, and the service handles the execution cycle. The quickstart also lists optional memory, gateway, credential, evaluator and related components, allowing you to add capabilities as the workload requires them.
A managed harness reduces the amount of runtime code your team operates, but it can constrain control over planning, retries, state transitions and framework-specific behavior. Validate how the harness exposes the hooks your application needs before treating it as a drop-in replacement for an existing runtime.
AgentCore Runtime for code-based agents
For teams that need their own orchestration logic, AgentCore supports a code-based path in which developers select a framework and deploy the resulting agent to AgentCore Runtime. The quickstart names Strands, LangGraph, Google ADK and OpenAI Agents SDK as framework choices. The documented CLI flow covers local testing, deployment, invocation, and access to logs and traces.
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This model separates framework ownership from infrastructure operations. Your team remains accountable for prompts, state transitions, tool authorization and dependency behavior, while AgentCore supplies the managed runtime boundary. Confirm framework version support, package size limits, startup behavior, networking and observability details for the selected deployment before estimating operational savings.
Microsoft Foundry Agent Service: three agent types
Prompt agents
Foundry prompt agents are configuration-led agents managed by the service. You select a model and define instructions, tools and resources without deploying a server that owns the agent loop. This is appropriate for teams that want a governed, repeatable configuration and do not need a custom framework runtime.
Voice prompt agents
Voice prompt agents add managed real-time voice orchestration. Voice behavior depends on the selected model, voice and Foundry resource region. Microsoft identifies voice-based functionality and some related monitoring, evaluation, avatar, WebRTC and telephony features as preview; preview services do not carry a service-level agreement and should not be treated as production-ready without checking the current terms.
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Hosted agents
Hosted agents let you bring code and a framework that Foundry runs behind a managed endpoint. You can provide a container image or a source ZIP from which Foundry builds an image. This suits an existing agent application that needs custom routing, private libraries or a framework not represented by prompt-agent configuration.
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Tools, memory and enterprise controls
Microsoft’s Agent Service overview documents model catalog access, web and file search, code interpreter, memory, custom functions, OpenAPI tools, MCP servers and reusable toolboxes. It also describes tracing, evaluation and publishing features. These components are useful only when their data residency, retention and identity behavior match your workload; inspect the settings for the specific Foundry resource and model.
Enterprise controls include Microsoft Entra identity, Azure role-based access control, content filters and virtual network isolation. Model and feature availability depends on the Foundry resource region, selected model and voice, subscription and any preview enablement. Do not infer availability in one region from a marketing catalog or from another Azure subscription.
Managed orchestration versus hosted agents
The most important architectural decision is who owns the agent loop. “Managed” does not mean “no code,” and “hosted” does not mean “self-managed servers.” Use this distinction to set team responsibilities.
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| Decision | Managed orchestration | Hosted customer runtime |
|---|---|---|
| Loop and planning | Provider executes the documented loop from configuration or API calls | Your code and framework define planning, routing, retries and state transitions |
| Application control | Fast to standardize, but behavior is bounded by exposed settings and hooks | Maximum control over middleware, custom policies and nonstandard workflows |
| Operations | Less runtime infrastructure to patch and scale | Provider operates the endpoint; you still maintain images, dependencies and application reliability |
| Portability | Tools, memory and resource formats may be provider-specific | Framework code can be moved, but model, identity, networking and tool integrations still require adaptation |
| Best fit | Standard assistants, governed retrieval and predictable tool calls | Existing agent products, proprietary planners, complex workflows or specialized libraries |
OCI lets a team combine both styles through the Responses API and hosted applications. AWS exposes both a managed harness and code-based AgentCore Runtime. Foundry offers prompt and voice prompt agents alongside hosted agents. The comparable question is therefore not “which cloud has agents?” but “which boundary gives our team the right control and responsibility?”
Comparison by the decisions an enterprise must make
1. Runtime ownership
Choose a managed loop when the task can be expressed with provider-supported instructions, tools and memory and your priority is reducing runtime code. Choose a hosted runtime when you must control the planner, call sequence, state machine, framework or middleware. An API-first design such as OCI Responses API is a middle ground: the surrounding application remains yours while selected agent functions are managed.
2. Model choice and portability
Build a region-specific model inventory before selecting a platform. Record the model identifier, deployment type, context limits, tool support, safety settings and quota for the exact region. A provider’s broad model-catalog statement does not prove that a particular model is available to your subscription, endpoint type or agent feature. Keep prompts and tool schemas versioned so that a model change can be evaluated rather than silently introduced.
3. Tools and integrations
Map every required action to an explicit integration:
- Retrieval over files and vector indexes
- Function or API calls with typed schemas
- MCP servers and reusable tool collections
- Sandboxed code execution
- Enterprise database queries, including NL2SQL where supported
- Cloud-native gateways, credentials and evaluators
OCI documents File Search, Function Calling, MCP Calling, Code Interpreter, managed vector stores and permission-controlled database queries. AgentCore documents gateway and credential components in addition to its runtime paths. Foundry documents web and file search, code interpreter, custom functions, OpenAPI tools, MCP servers and reusable toolboxes. Compare the integration you actually need, not the number of items in a feature list.
4. State, memory and data boundaries
Separate transient conversation context from durable memory, uploaded files, vector indexes and business records. For each store, document retention, deletion, tenant isolation, encryption, backup, export and administrator access. Decide whether the agent may write durable memory automatically or only through an approved application function. OCI documents conversation context, long-term memory and short-term context compaction; AWS lists optional memory components; Foundry documents memory. The names are not proof that retention or semantics are equivalent.
5. Identity and network controls
Give every agent and tool connector the least privilege needed for one task. Validate how the platform authenticates model calls, hosted runtimes, MCP servers, databases and outbound APIs. OCI managed applications support public and private endpoints according to the GA announcement. Foundry documents Entra identity, Azure RBAC and virtual network isolation. AgentCore deployments need an equivalent review of runtime identity, gateway policy, credentials and network paths. Test a denied action as deliberately as a successful action.
6. Lifecycle maturity and regional fit
Mark each dependency as generally available, preview, or restricted to existing customers. AWS Classic’s new-customer restriction is material for a fresh design. Foundry preview features may have no SLA. OCI’s nine-region announcement does not eliminate the need to verify current model and capability coverage. Freeze a dated compatibility matrix in the architecture record and recheck it before launch and after major platform updates.
7. Observability and evaluation
Capture the agent request, model choice, tool calls, arguments after redaction, approvals, latency, token or inference usage, retries, final answer and failure reason. Use provider tracing where available, then export a common event format so two platforms can be compared. Include adversarial prompts, permission-boundary tests, retrieval-grounding checks and tool-timeout scenarios in the acceptance suite.
Best Value
Which platform fits common enterprise situations?
Choose OCI when your architecture is OCI-centered or needs a split design
OCI is a strong candidate when your data and identity already live in OCI, you want an OpenAI-compatible API surface, or you need to combine managed Responses API functions with a containerized custom runtime. Its documented vector stores, NL2SQL path and database permission controls are relevant to data-heavy enterprise assistants. Confirm that the required models, regions and private endpoint behavior are available before committing.
Choose AWS AgentCore when framework deployment and AWS integration are priorities
AgentCore fits teams that want a managed harness for standard flows or need to deploy code built with one of the documented frameworks. The local-test, deploy, invoke and logs/traces workflow can support an existing engineering pipeline. New customers should plan against AgentCore rather than Bedrock Agents Classic and verify the exact memory, gateway, credential and evaluator components required by the design.
Choose Foundry Agent Service when Azure governance or voice is central
Foundry is a natural fit for organizations standardized on Entra identity, Azure RBAC and virtual network controls, or for teams that need a single service covering prompt, voice prompt and hosted agents. Treat preview voice and adjacent features as a separate risk decision, with an explicit production fallback if the preview terms or regional availability change.
A repeatable evaluation plan
Do not compare vendors with different prompts, models or permission scopes. Run a bounded pilot that produces evidence your architecture review can audit.
- Define one representative task. Specify the user, business outcome, allowed data, required tools, maximum latency, escalation path and unacceptable actions.
- Normalize the model variable. Use the same model class where all three platforms support it, or document the model difference and treat it as a separate variable. Record region, version and quota.
- Build identical tool contracts. Use the same function names, schemas, mock data, timeout limits and approval rules. Keep write operations disabled until read-only behavior passes.
- Set equivalent memory and retrieval conditions. Load the same corpus, chunking policy, metadata filters, retention period and conversation history. Measure citation or grounding errors rather than relying on a single successful answer.
- Exercise failure paths. Deny a permission, return malformed tool data, induce a timeout, exceed a context limit and revoke a credential. Record whether the agent stops safely, retries, explains the failure and leaves durable state.
- Measure the whole service. Track task success, factuality, latency distribution, recovery rate, inference and tool cost, hosted compute, storage, observability spend and operator minutes. A lower model bill can be offset by runtime or support work.
- Review security and operations. Inspect logs for sensitive data, verify network egress, rotate credentials, test tenant isolation and confirm who can publish or change an agent.
- Document portability. List provider-specific prompts, memory formats, tool adapters, identity bindings and deployment manifests. Estimate the work to reproduce the agent on a second platform before calling it portable.
Cost and operating model
None of the cited product documentation supports a normalized price ranking. Build a workload model that includes model inference, tool and gateway calls, hosted runtime compute, vector and file storage, memory, network transfer, logs, traces, evaluations, database queries and the engineering time needed to operate the system. Use the current pricing calculators and regional terms for the target configuration; promotional or one-time allowances should not be treated as recurring economics.
Managed orchestration can reduce infrastructure work while increasing dependence on provider-specific memory, tools or tracing. Hosted runtimes can improve control and portability of application code while adding image builds, patching, scaling policy and incident response. Put both labor and cloud charges in the same total-cost worksheet.
Common pitfalls and how to avoid them
- Evaluating Bedrock Agents Classic for a new project: use AgentCore guidance instead, because AWS says Classic is closed to new customers.
- Assuming a feature name means identical behavior: verify retention, limits, supported models, tool schemas and regional availability for the exact service.
- Giving an agent broad database or API rights: place every write behind a narrowly scoped function, approval policy and audit trail.
- Treating hosted as serverless magic: maintain dependencies, secrets, health behavior, safe retries and image provenance for the customer runtime.
- Using preview voice or monitoring features as production foundations: check the current SLA and keep a fallback path.
- Comparing only model responses: include denied actions, tool failures, recovery, observability and operator effort.
- Ignoring regional constraints: verify the model, endpoint type, memory, tool and network feature in the intended region and subscription.
Decision checklist
Before approving a platform, require written answers to these questions:
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- Which exact models and agent features are available in the production region?
- Where are conversations, files, vectors and durable memories stored and deleted?
- How are agent identity, tool credentials, database permissions and network egress enforced?
- Which required capabilities are generally available, and which are preview or restricted?
- What is the measured cost per completed task, including hosting and operations?
- How will traces, evaluations, rollback and incident response work?
- What provider-specific code or data formats would make a future migration difficult?
Bottom line
OCI is differentiated by pairing an API-first Responses API with managed hosting for custom agent applications and by documenting enterprise data features such as vector stores, NL2SQL and permission-controlled database access. AWS’s current new-customer path is AgentCore, with both a managed harness and framework-based code deployment. Foundry Agent Service spans prompt, voice prompt and hosted agents with Azure identity, networking and governance controls. Select the service that matches your desired runtime ownership, verified regional model and tool availability, data boundaries and measured total cost; validate those choices with the same task and permissions on every platform.
Quick Recap
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.
