Introduction
The best AI agent platforms help businesses build systems that can understand a goal, select appropriate tools, complete several steps, and return a useful result with limited human intervention.
For code-first development, OpenAI’s Agents SDK is a strong option because it combines tools, handoffs, guardrails, state, tracing, and controlled execution. LangGraph is better suited to teams that need detailed workflow control, persistent state, checkpoints, and human approval.
Google Vertex AI Agent Builder, Microsoft Foundry Agent Service, and Amazon Bedrock AgentCore are stronger choices for organisations already operating inside a major cloud ecosystem.
Salesforce Agentforce is particularly relevant for companies that want AI agents connected to customer data and CRM workflows. Meanwhile, AI by Zapier provides a more accessible route for no-code business automation.
For ecommerce support, Gorgias AI Agent is the most specialised platform in this comparison. It connects customer conversations with Shopify data, orders, products, inventory, returns, discounts, and other commerce workflows.
People searching for the best AI agents platforms should therefore avoid selecting a product based only on model quality.
A production agent also needs:
- Secure tool access
- Reliable state management
- Human approval controls
- Audit logs
- Testing and evaluation
- Error recovery
- Cost monitoring
- Data protection
- Clear escalation rules
The best platform depends on what the agent must do and how much risk it is allowed to take.
This guide compares eight leading options, explains how agent platforms work, and examines important topics such as audit logging, deterministic replay, ecommerce support, and multi-agent orchestration.
What Is an AI Agent Platform?
An AI agent platform is a development or business environment for creating software that can reason about a task, select tools, and take actions.
A normal chatbot mainly responds with text.
An AI agent may also:
- Search company documents
- Read files
- Call an API
- Update a CRM
- Create a support ticket
- Run code
- Check an order
- Schedule a meeting
- Request approval
- Hand work to another agent
- Continue a long-running task
An agent platform provides the infrastructure around the language model.
The main components of an AI agent
A production AI agent usually contains several parts.
Model
The model interprets instructions and decides what to do next.
It may be an OpenAI, Google, Anthropic, Meta, Amazon, or other supported model, depending on the platform.
Instructions
Instructions define the agent’s role, boundaries, priorities, and expected behaviour.
For example:
Resolve routine order-status requests. Do not issue a refund above the approved limit. Escalate suspected fraud to a human representative.
Tools
Tools allow the agent to interact with external systems.
They may include:
- Search
- Databases
- Calendars
- CRMs
- Ecommerce systems
- Code execution
- Browsers
- Internal APIs
- Model Context Protocol servers
Memory and state
State records what has happened during the current workflow.
Memory may preserve useful information across multiple sessions.
These features allow the agent to continue a task without repeatedly starting from the beginning.
Orchestration
Orchestration controls the order in which models, tools, people, and other agents participate.
Some platforms use a flexible agent loop. Others allow developers to define a graph with explicit routes and checkpoints.
Guardrails
Guardrails prevent or pause unsafe actions.
For example, an agent may be allowed to draft a refund but require human approval before issuing it.
Observability
Observability records what happened during a run.
It may show:
- Model calls
- Tool calls
- Inputs and outputs
- Errors
- Retries
- Latency
- Token usage
- Approval decisions
- State transitions
OpenAI’s Agents SDK, for example, provides built-in tracing across model calls, tools, handoffs, guardrails, and custom workflow spans.
How AI Agent Platforms Work
Although platforms differ, most agent workflows follow a similar process.
Step 1: The user or system provides a goal
An agent begins with a request or trigger.
Examples include:
- Find five suitable sales prospects.
- Resolve this customer’s delivery problem.
- Compare three supplier proposals.
- Review these documents for missing information.
- Prepare a weekly market report.
- Update our CRM after a successful call.
The goal should be specific enough to guide the system.
Step 2: The agent reviews its available context
The platform may provide:
- User instructions
- Conversation history
- Company policies
- Connected documents
- Customer information
- Tool descriptions
- Saved memory
The agent uses this context to decide how to approach the task.
Step 3: The model creates a plan or selects a tool
The model may decide to search a database, read a document, call an API, or ask the user for missing information.
The platform then validates whether the agent is permitted to use that tool.
Step 4: The agent performs an action
A tool may:
- Retrieve information
- Update a record
- Send a message
- Execute code
- Add an item to a database
- Schedule an appointment
The result returns to the agent.
Step 5: The agent evaluates the result
The agent checks whether the task is complete.
It may call another tool, hand the task to a specialist agent, request human approval, or produce a final response.
Step 6: The platform records the run
A production platform should record the agent’s execution path.
This allows developers and administrators to investigate:
- Why a tool was selected
- Which data was accessed
- What the tool returned
- Whether retries occurred
- Who approved the action
- Why the workflow failed
Amazon Bedrock AgentCore Observability, for example, records traces, sessions, spans, tool invocations, execution paths, errors, latency, token usage, and other operational details through OpenTelemetry-compatible telemetry.
Why the Best AI Agent Platforms Matter
AI agents can affect external systems.
This makes platform choice more important than selecting a normal chatbot.
Agents can perform real work
A chatbot may incorrectly answer a question.
An agent may incorrectly:
- Cancel an order
- Send an email
- Change a customer record
- Purchase a service
- Delete a file
- Issue a refund
- Publish content
Therefore, agents need stronger controls than ordinary text generators.
Long workflows need state and recovery
A task may require several model and tool calls.
If the system fails halfway through, the platform should preserve enough state to continue safely.
OpenAI’s updated Agents SDK supports controlled sandbox environments, persistent state, snapshots, and rehydration for longer tasks. The separation between the agent harness and execution environment is also designed to reduce security and credential exposure.
Enterprises need governance
Large organisations need to control:
- Which models may be used
- Which tools agents can access
- Which employees can publish agents
- Where data is stored
- Which actions require approval
- How activity is audited
Microsoft Foundry Agent Service includes managed identity, role-based access, network controls, guardrails, tracing, metrics, and Application Insights integration.
Agents need continuous evaluation
An agent may perform well during a short demonstration but fail in production.
Real users may provide:
- Incomplete requests
- Conflicting instructions
- Misspellings
- Unexpected formats
- Malicious prompts
- Unavailable tools
- Duplicate actions
The platform should support testing, traces, evaluations, and controlled rollouts.
Main Benefits of AI Agent Platforms
1. Automation of multistep work
Traditional automation follows fixed steps.
An AI agent can choose among several tools depending on the situation.
For example, a customer-support agent may:
- Identify the customer.
- Check the order.
- Review the delivery status.
- Search the return policy.
- Decide whether the case needs escalation.
- Draft or perform the next approved action.
2. Natural-language interfaces
Users can ask an agent to perform work without learning every part of the connected software.
This makes complex systems easier to access.
However, natural language should not remove confirmation steps for sensitive actions.
3. Better connection between software tools
Agents can connect CRMs, calendars, documents, databases, support platforms, and communication channels.
Zapier’s agent tools are built around its broad app ecosystem, allowing agents to use connected applications as actions and knowledge sources. Zapier is currently moving its standalone Agents experience into AI by Zapier workflows, where tool calls, task usage, approvals, and run history are managed inside Zaps.
4. Faster research and analysis
Agents can gather information from several sources, compare results, and prepare structured summaries.
A human should verify conclusions before using them for legal, medical, financial, or other high-impact decisions.
5. Human escalation
Good agent platforms do not force full automation.
They allow workflows to pause when:
- Confidence is low
- A policy exception appears
- The action is expensive
- Personal data is involved
- The customer asks for a person
- The issue involves risk or safety
6. Reusable specialist agents
Organisations can build separate agents for:
- Research
- Customer service
- Sales
- Finance
- Human resources
- IT support
- Procurement
- Document review
A coordinating agent may then route tasks to the appropriate specialist.
7. Better visibility into automated decisions
Traces and audit logs help teams investigate unexpected behaviour.
They also support quality improvement, security reviews, incident response, and regulatory compliance.
Major Risks and Limitations
1. Incorrect reasoning
An agent may misunderstand a request or choose the wrong tool.
Its explanation may sound confident even when the underlying decision is weak.
High-impact actions should therefore use clear rules and human review.
2. Prompt injection
Malicious or untrusted content may contain instructions intended to manipulate the agent.
For example, a webpage could tell a browsing agent to ignore its rules and reveal private data.
Agent systems should treat external content as untrusted and restrict the tools and credentials available to the model.
3. Excessive permissions
An agent should receive only the permissions it needs.
A support agent that checks order status should not automatically receive permission to delete accounts or export the entire customer database.
4. Duplicate or repeated actions
A retry may accidentally perform the same action twice.
This can create:
- Duplicate emails
- Repeated payments
- Multiple refunds
- Duplicate records
- Repeated orders
High-risk tool calls should use idempotency keys or another mechanism that prevents the same transaction from being completed twice.
5. Uncontrolled costs
Long-running loops can consume substantial model and tool resources.
Platforms should support:
- Maximum step limits
- Time limits
- Token budgets
- Tool-call limits
- Approval thresholds
- Cost alerts
AI by Zapier now pauses an individual run for review when it exceeds a defined task threshold, while its history can show task use by tool call, model tier, and workflow step.
6. Weak observability
Without detailed tracing, it may be impossible to determine why the agent failed.
A final output alone does not reveal:
- Which tools were called
- Which results were returned
- Which instructions were active
- Where latency occurred
- Whether the system retried
- What state was changed
7. Sensitive information in logs
Detailed traces may contain customer messages, tool arguments, model responses, or internal documents.
Microsoft recommends redacting secrets and personal information before telemetry is stored, restricting access to traces, and applying formal retention policies.
8. Platform dependence
A business may become dependent on one provider’s:
- Models
- Tool format
- Memory system
- Observability service
- Pricing structure
- Deployment environment
Open standards such as MCP, OpenTelemetry, and agent-to-agent protocols may reduce some of this dependence.
Real-World AI Agent Use Cases
Customer support
An AI support agent can answer questions, check account information, guide troubleshooting, and escalate complex cases.
Ecommerce support
An ecommerce agent may:
- Track orders
- Check inventory
- Recommend products
- Process eligible returns
- Change subscriptions
- Apply an approved discount
- Escalate damaged-item claims
Gorgias combines an ecommerce help desk with an AI agent that can access Shopify customer, order, inventory, and product information. It also connects with commerce tools for actions such as refunds, subscription changes, discounts, shipping updates, and product recommendations.
Employee support
An internal agent can answer questions about:
- IT access
- Company policies
- Benefits
- Expenses
- Payroll
- Procurement
- Onboarding
Sales and CRM management
An agent may research accounts, draft personalised outreach, update CRM records, schedule meetings, and prepare follow-up tasks.
Salesforce Agentforce supports customer service, employee support, appointment scheduling, sales development, product recommendations, and other workflows connected to Salesforce data and actions.
Research and document analysis
A research agent can gather files, search approved sources, compare documents, extract information, and prepare an evidence-based report.
Software development
Developer agents can inspect repositories, run tests, edit code, and work inside controlled execution environments.
Back-office automation
No-code agents can assist with:
- Lead routing
- Invoice processing
- Report preparation
- Data entry
- Email classification
- Project updates
Best AI Agent Platforms in 2026
1. OpenAI Agents SDK — Best for OpenAI-First Development
The OpenAI Agents SDK is a code-first framework for developing tool-using agents in Python or TypeScript.
It supports:
- Tools and MCP connections
- Agent handoffs
- Guardrails
- Human approvals
- Sessions and resumable state
- Voice agents
- Tracing
- Sandboxed execution
OpenAI recommends the SDK for bounded conversational or transactional workflows where developers want control over tools, state, deployment, storage, and approval decisions.
Best for
- Product developers
- OpenAI-based applications
- Research agents
- Coding agents
- Multi-agent handoffs
- Custom business workflows
Main limitation
The SDK is developer-focused. Teams must still design their own application, access controls, data storage, and production infrastructure.
2. Google Vertex AI Agent Builder — Best for Google Cloud
Vertex AI Agent Builder provides tools for building, deploying, scaling, and governing AI agents on Google Cloud.
Google’s Agent Development Kit supports models, tools, sessions, memory, and stateful interactions. Agents can be deployed to a managed runtime using Google Cloud infrastructure.
Best for
- Google Cloud customers
- Gemini-based agents
- Enterprise search
- Stateful applications
- Managed deployment
- Multimodal workflows
Main limitation
The platform is most attractive when the organisation is already comfortable with Google Cloud identity, billing, storage, and deployment services.
3. Microsoft Foundry Agent Service — Best for Microsoft Enterprises
Microsoft Foundry Agent Service is a managed platform for building and hosting AI agents.
It supports prompt-based agents and hosted agents developed using several frameworks, including LangGraph and the OpenAI Agents SDK.
The platform offers:
- Multiple model options
- Managed runtime
- File and web tools
- Code execution
- MCP connections
- Memory
- Microsoft Entra identity
- Role-based access
- Tracing
- Application Insights
- Microsoft 365 and Teams distribution
Best for
- Azure customers
- Microsoft 365 organisations
- Enterprise identity requirements
- Multi-framework development
- Governed internal agents
- Teams-based distribution
Main limitation
Microsoft’s agent product names and platform architecture have changed rapidly. Teams should verify the current service status and whether any selected feature remains in preview.
4. Amazon Bedrock AgentCore — Best for AWS Infrastructure
Amazon Bedrock AgentCore is a managed platform for deploying and operating agents using different models and frameworks.
Its components include:
- Runtime
- Memory
- Gateway
- Identity
- Browser
- Code Interpreter
- Observability
- Secure tool connectivity
AgentCore Gateway can expose APIs, AWS Lambda functions, and MCP tools to agents. AgentCore Identity helps agents access AWS and third-party services with managed credentials.
Best for
- AWS customers
- Secure enterprise agents
- Browser automation
- Code-execution agents
- Multi-framework deployments
- CloudWatch-based monitoring
Main limitation
The number of AWS services and configuration choices can make initial architecture more complex for smaller teams.
5. LangGraph and LangSmith — Best for Controlled Orchestration
LangGraph is a low-level orchestration framework for long-running, stateful agents.
Its main strength is the ability to combine deterministic software logic with flexible model-driven steps.
It provides:
- Durable execution
- Persistent state
- Human-in-the-loop controls
- Memory
- Checkpoints
- Streaming
- Time travel
- Custom graph structures
LangSmith adds tracing, evaluations, monitoring, and deployment support around LangGraph applications.
Best for
- Complex agent workflows
- Custom orchestration
- Human approvals
- Long-running tasks
- Stateful applications
- Debugging and evaluation
Main limitation
LangGraph provides more control than a no-code product, but it requires developers to design the graph, state, tool boundaries, and deployment architecture.
6. Salesforce Agentforce — Best for CRM and Enterprise Service
Salesforce Agentforce connects autonomous agents with customer data, CRM records, business processes, and communication channels.
Its platform includes:
- Agent Builder
- Agent Script
- Voice
- Observability
- MCP support
- Multi-agent orchestration
- Customer and employee workflows
- Salesforce Data 360 integration
Best for
- Salesforce customers
- Customer service
- Sales workflows
- CRM automation
- Employee support
- Enterprise customer data
Main limitation
Agentforce delivers the strongest value when the organisation’s customer information and workflows already operate within Salesforce.
7. AI by Zapier — Best No-Code AI Agent Builder
Zapier allows non-developers to create AI-powered workflows connected to thousands of applications.
Users can define instructions, connect actions and knowledge, test the workflow, and publish it without writing a full application.
As of July 2026, Zapier is transitioning its standalone Agents product into AI by Zapier steps inside the main Zap editor. The new workflow provides approval controls, tool-call visibility, task-level cost information, and centralised run history.
Best for
- Small businesses
- No-code automation
- Marketing operations
- Administrative workflows
- Cross-app processes
- Rapid prototypes
Main limitation
Complex stateful or highly customised agent systems may require a developer framework rather than a no-code workflow builder.
8. Gorgias AI Agent — Best for Ecommerce Support
Gorgias AI Agent is built specifically for ecommerce customer service and conversational commerce.
It can use:
- Shopify data
- Product catalogues
- Inventory
- Customer history
- Order information
- Brand policies
- Help-centre content
The agent can also connect with ecommerce tools to perform approved actions instead of only answering questions.
Best for
- Shopify stores
- Direct-to-consumer brands
- Order support
- Product recommendations
- Returns and subscription questions
- Ecommerce customer service
Main limitation
Gorgias is a specialised commerce-support platform rather than a general-purpose developer environment.
AI Agent Platform Comparison Table
| Platform | Best for | Development style | Managed runtime | Human approval | Observability |
|---|---|---|---|---|---|
| OpenAI Agents SDK | OpenAI-first applications | Code-first | Developer selected | Yes | Built-in tracing |
| Google Vertex AI Agent Builder | Google Cloud agents | Code and managed tools | Yes | Configurable | Cloud-based monitoring |
| Microsoft Foundry Agent Service | Microsoft enterprises | Low-code and code | Yes | Yes | Application Insights tracing |
| Amazon Bedrock AgentCore | AWS-native deployments | Multi-framework | Yes | Configurable | CloudWatch and OpenTelemetry |
| LangGraph and LangSmith | Controlled orchestration | Code-first graph | Cloud or self-managed | Strong | Detailed tracing and evaluation |
| Salesforce Agentforce | CRM and customer operations | Low-code and pro-code | Yes | Configurable | Agentforce Observability |
| AI by Zapier | No-code business automation | No-code | Yes | Per-tool approvals | Zap history and task details |
| Gorgias AI Agent | Ecommerce support | Configured platform | Yes | Escalation and policies | Ecommerce-focused analytics |
Which AI Agents Are Best for Ecommerce Support?
Gorgias AI Agent is the strongest specialist option for Shopify-focused ecommerce brands because its customer-service workflow is connected directly to store, order, inventory, and product information.
Salesforce Agentforce may be a better fit for larger organisations that already use Salesforce for commerce, service, and customer data.
AI by Zapier is useful when a store needs a custom workflow across ecommerce, email, spreadsheets, fulfilment, and marketing tools.
The right selection depends on:
- Ecommerce platform
- Monthly ticket volume
- Support channels
- Existing CRM
- Refund and return policies
- Subscription tools
- Human-support team
- Required integrations
An ecommerce business should test the agent on real scenarios such as lost packages, partial refunds, cancelled subscriptions, incorrect items, and inventory questions before allowing it to take actions automatically.
AI Agent Audit Log Best Practices
An audit log should make it possible to reconstruct what the system knew, what it did, and who authorised the action.
At minimum, record:
- Unique run and session IDs
- Timestamp
- User, tenant, or service identity
- Agent and workflow version
- Model provider and model identifier
- System-instruction version
- Tool definitions and versions
- Input and relevant context
- Tool calls and arguments
- Tool results
- State transitions
- Retries and errors
- Human approvals
- Policy and guardrail decisions
- Output
- Latency and token usage
- Final action status
OpenTelemetry’s generative-AI conventions standardise telemetry for model names, token usage, tool calls, outputs, timing, and agent spans. Detailed content capture is optional because prompts, arguments, and outputs may contain sensitive information.
Separate operational telemetry from sensitive content
Operational logs may record:
- Model
- Duration
- Status
- Token count
- Tool name
- Error code
Detailed content logs may include:
- Full prompts
- Customer messages
- Tool arguments
- Tool outputs
These should have stricter access and retention controls.
Version every important component
Record the active versions of:
- Instructions
- Prompts
- Tools
- Policies
- Guardrails
- Models
- Knowledge sources
Without version information, a historical run may be impossible to investigate accurately.
Use immutable records for high-risk actions
For financial or regulated actions, maintain a tamper-resistant record of:
- Requested action
- Approval
- Exact parameters
- External response
- Final status
Redact secrets and unnecessary personal data
Do not place passwords, API keys, authentication tokens, or unnecessary personal information inside traces.
Treat agent logs as sensitive production data.
Deterministic Replay for AI Agents
A deterministic system produces the same result when given the same inputs.
True deterministic replay is difficult for AI agents because:
- Language-model outputs may vary
- Models may be updated
- Search results can change
- Databases can change
- External APIs can return different responses
- Time-dependent tools produce new values
- Tool side effects may already have occurred
LangGraph supports replaying or forking from stored checkpoints. However, downstream nodes are re-executed, including model and API calls, so the results may differ from the original run.
Therefore, organisations should distinguish between two concepts.
Execution reconstruction
Execution reconstruction means reviewing the exact historical:
- Inputs
- Outputs
- Tool results
- State
- Decisions
- Approvals
This relies on stored audit records.
Workflow re-execution
Workflow re-execution means running the workflow again from a checkpoint.
The new result may differ if models or external systems have changed.
Best practices for reliable replay
To improve reproducibility:
- Store the exact model identifier and parameters.
- Version instructions, tools, and policies.
- Save external tool responses when permitted.
- Record timestamps and environment information.
- Use idempotency keys for actions.
- Mock external tools during testing.
- Replay inside an isolated environment.
- Separate read-only replay from live execution.
- Require approval before repeating side effects.
- Preserve the original run as an immutable record.
The objective should not always be identical output.
A more practical objective is to reproduce the same evidence, workflow state, and decision context closely enough to understand why the agent behaved as it did.
Best Practices for Selecting an AI Agent Platform
Define the use case before choosing a platform
Start with the workflow rather than the product.
Describe:
- Input
- Required tools
- Expected output
- Risk level
- Human approval
- Data sensitivity
- Success measurement
Begin with read-only access
A new agent should first search and recommend.
Only add write permissions after the agent has been tested.
Limit tool permissions
Give the agent access only to the functions required for its role.
Add approval for irreversible actions
Require confirmation for:
- Payments
- Refunds
- Deletions
- Account changes
- External messages
- Purchases
- Legal submissions
Build evaluation datasets
Test the agent against realistic examples, including:
- Normal requests
- Missing information
- Conflicting instructions
- Malicious prompts
- Tool failures
- Policy exceptions
- Multilingual input
Monitor business outcomes
Do not measure only successful tool calls.
Track:
- Correct resolution
- Error rate
- Escalation rate
- Repeated cases
- Customer satisfaction
- Human correction
- Cost per completed task
Create a shutdown process
Administrators should be able to disable an agent or individual tool quickly.
Future Trends in AI Agent Platforms
More multi-agent orchestration
Businesses will use teams of specialist agents rather than one system trying to manage every task.
Wider adoption of open protocols
MCP and agent-to-agent protocols will make it easier to connect agents with tools and other systems.
Stronger identity management
Agents will increasingly receive their own controlled identities, permissions, and credentials rather than sharing a human account.
Standardised observability
OpenTelemetry-compatible traces will help organisations monitor agents across several frameworks and cloud providers.
More deterministic workflow boundaries
Businesses will combine flexible model reasoning with deterministic rules.
LangGraph’s ability to mix agentic and fixed software steps reflects this direction.
Increased human supervision
Instead of choosing between full automation and manual work, organisations will add approvals at specific risk points.
Better ecommerce agents
Ecommerce agents will continue moving from answering FAQs toward completing transactions, recommending products, managing subscriptions, and assisting with returns.
Frequently Asked Questions
What are the best AI agent platforms in 2026?
The leading options include OpenAI Agents SDK, Google Vertex AI Agent Builder, Microsoft Foundry Agent Service, Amazon Bedrock AgentCore, LangGraph, Salesforce Agentforce, AI by Zapier, and Gorgias AI Agent.
The best platform depends on the organisation’s cloud, development skills, data, integrations, and use case.
Which AI agent platform is best for developers?
OpenAI Agents SDK is a strong option for OpenAI-based applications.
LangGraph is more suitable when developers require precise graph orchestration, persistent state, checkpoints, and human intervention.
Which AI agents are best for ecommerce support?
Gorgias AI Agent is the strongest specialised option for Shopify-focused ecommerce support.
Salesforce Agentforce may be better for larger companies operating inside Salesforce, while AI by Zapier can support customised cross-application workflows.
What should an AI agent audit log contain?
It should record identities, timestamps, model and workflow versions, tool calls, arguments, results, approvals, errors, retries, state transitions, policies, outputs, and final action status.
Sensitive information should be redacted or protected through strict access and retention controls.
Can an AI agent run be replayed exactly?
Not always.
Model outputs, external APIs, search results, and data sources may change.
Checkpoints can support workflow replay, but exact historical reconstruction depends on saving the original inputs, outputs, tool responses, versions, and state.
Are no-code AI agent builders suitable for businesses?
Yes, especially for low-risk workflows involving common business applications.
However, complex systems with sensitive data, custom logic, or long-running state may require a developer-focused platform.
Do AI agents replace human employees?
AI agents can automate parts of a role.
Humans remain important for judgment, exceptions, accountability, customer relationships, safety decisions, and workflow design.
Conclusion
The best AI agent platforms are not simply the products with the most advanced models.
A strong platform must combine reasoning with secure tools, state management, approvals, monitoring, recovery, testing, and governance.
OpenAI Agents SDK is a strong code-first choice for OpenAI applications. Google Vertex AI Agent Builder, Microsoft Foundry Agent Service, and Amazon Bedrock AgentCore are suitable for organisations committed to their respective cloud ecosystems.