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Choose an AI agent platform by tracing a real business task from data retrieval to the final action. The winning platform is not the one with the longest connector catalog; it is the one that exposes the exact operations you need, runs them under the correct identity, rechecks authorization at execution time, and gives administrators practical controls to monitor and revoke access.
Document two or three representative workflows, then validate each candidate in your actual tenant, source systems, user groups, and deployment channel. Connector names, feature checklists, and vendor security statements are not enough to prove that a specific agent can safely perform a specific operation.
What you are actually choosing
An AI agent platform combines a reasoning model with tools that read data or take actions in other systems. Those tools may be native connectors, custom connectors, REST APIs, plug-ins, or Model Context Protocol (MCP) servers. Each integration has its own supported operations, authentication method, permission scope, policy checks, and maintenance requirements.
Selection therefore has four separate questions:
- Coverage: Can the agent reach every required system and perform the precise read or write operation?
- Identity: Does it act as the requesting employee, the person who built the agent, or a service identity?
- Enforcement: Which source-system permissions and platform policies are checked when the call runs?
- Control: Can administrators approve, monitor, restrict, rotate, disable, and remove the agent and its connections?
A connector listed in a catalog answers none of those questions by itself. Treat every integration as a technical and governance dependency that must be validated in the target environment.
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Start with a workflow, not a vendor list
Describe the task end to end
Write the business request in concrete terms. For example, “When a support manager asks for overdue renewals, read account data, filter it by the manager’s region, create a follow-up task, and post a confirmation in the team channel.” Identify every system read, transformation, write, notification, and human approval. Avoid a vague requirement such as “connect to our CRM.”
Record the data and action boundaries
For each step, record the data class, the minimum fields required, the allowed operation, and the users who may invoke it. A read-only summary and a record-deletion action should not share an unexamined connector scope. Note whether the source system already has row-level, field-level, or sharing rules that must remain effective.
Map the deployment channel
Specify where the agent will run: an employee portal, a collaboration channel, an embedded application, an API, or another assistant invoking it through MCP. Authentication and policy behavior can change by channel, so “the platform supports this” is incomplete without the channel name.
Define the failure and approval path
Decide what happens when a user lacks access, a connector is unavailable, or an action has material consequences. A safe design may return a denial, request human approval, or offer a read-only alternative. Capture these outcomes before comparing products.
Use an evidence-based comparison scorecard
Score each candidate against the same workflow. Mark a capability as confirmed only when documentation and a tenant-level test establish it; otherwise record the uncertainty and its owner.
| Axis | Questions to answer | Evidence to collect |
|---|---|---|
| Workflow and integration fit | Are all required systems connected? Are the exact read and write operations supported and maintained? | Connector documentation, operation list, version status, owner, and a successful test in your tenant. |
| Identity and scope | Does the agent act as an end user, maker, or service identity? Can access be limited to individual operations? | Authentication flow, token subject, consent screen, connector scopes, and service-account policy. |
| Runtime enforcement | Which source permissions and platform policies are rechecked when a call executes? Does enforcement vary by channel or connector type? | Denied-access test, runtime logs, policy configuration, and documented channel limitations. |
| Governance lifecycle | Who may create, publish, share, approve, disable, or delete agents and connections? | Role matrix, approval workflow, user-eligibility controls, and offboarding procedure. |
| Operations and observability | Can administrators see failures, activity, usage, synchronization, and policy blocks? Can credentials be rotated or revoked? | Audit events, dashboards, alerting, retention, credential process, and telemetry terms. |
| Fit and operating effort | Can your team maintain connectors, policies, prompts, identities, and upgrades over time? | Named owners, support model, change calendar, training needs, and estimated administration hours. |
Do not collapse the scorecard into a generic integration count. A platform with fewer connectors may be safer and more useful if its required operations preserve source authorization and are easier to operate.
Verify integration fit at the operation level
Catalog presence is only a starting point
Open the documentation for every required connector and list the supported operations. Check whether the operation is read-only or mutating, whether it supports filters and pagination, which objects or fields it exposes, and whether it is generally available or in preview. Confirm the connector’s maintenance owner and deprecation process.
Check authentication and credential ownership
Determine who creates the connection, where credentials are stored, and whose account is used for each call. Ask whether users must consent individually, whether an administrator grants tenant-wide consent, and how a departed employee’s access is removed. A shared service credential can simplify deployment while creating a broad blast radius; delegated user access can preserve least privilege while increasing consent and troubleshooting work.
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Separate native, custom, and external tools
Native connectors may receive platform-specific policy treatment that custom connectors, MCP servers, or REST tools do not. Document the tool type beside each workflow step and test it in the intended channel. Never infer that a control documented for a native connector automatically applies to an externally hosted tool.
Assess reliability and change risk
Record rate limits, timeouts, retry behavior, pagination limits, schema-change notices, and environment differences between development and production. An agent that can technically call a system but fails during peak load is not an integration fit. Assign an owner for connector updates and a rollback plan for breaking changes.
Control what the agent can access
Identify the acting principal
Ask the platform owner to show the identity presented to the source system. It should be clear whether the call uses the authenticated requester, the agent maker, or a service identity. “The agent has access” is not a sufficient answer because each model produces different audit records and separation-of-duty risks.
Prefer operation-specific scope
Where possible, grant only the operations required by the workflow. A connector permission that covers an entire API or object family may be substantially broader than a permission for one action. Examine whether scopes are attached to individual operations, a connector, an environment, or a tenant-wide application.
Preserve source-system authorization
Test with two accounts that have different source permissions. The lower-privilege account should receive a denial or filtered result when it lacks access; the agent should not disclose data merely because its maker or service credential can see it. Confirm field-level, record-level, sharing, and license checks where the source system supports them.
Distinguish setup permissions from execution permissions
A platform may grant an identity permission to be configured with a tool while separate policies decide whether a particular call is allowed at runtime. Review both layers. A green status in an administration screen proves configuration, not that every user request will be authorized.
Understand runtime enforcement and channel differences
Runtime enforcement is the point at which the platform and source system evaluate the actual call, user, resource, and policy state. Ask which rules are rechecked, where a denial is logged, and whether a policy decision occurs before or after the model generates a tool request.
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Channel differences deserve their own test matrix. Run the same low-risk read and denied-access case in each planned channel. Compare the identity, token claims, policy result, and audit event. A permission model that works in a collaboration channel may use a different connector authentication flow in a custom web channel or API.
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Microsoft Copilot Studio example
Microsoft documents that new Copilot Studio agents automatically receive Microsoft Entra Agent IDs. Agents created before Agent ID became available in May 2026 continue using legacy app registrations until a future migration. When an agent is published, its Agent ID receives permissions representing the Power Platform connectors configured for that agent. Depending on configuration, a scope can represent an entire connector or specific operations.
Microsoft also says connector runtime revalidates calls against tenant advanced connector policies and data policies. Its documentation summarizes the distinction this way: “In short, the scopes describe what an agent is configured to do whereas advanced connector policies and data policies decide what it’s allowed to do at the moment of execution.” See Microsoft’s documentation on app registration, agent identities, and authentication.
There is an important channel qualification. The documented Conditional Access enforcement on the agent identity currently applies only when an agent runs in Teams. Other channels continue to call connectors through the existing Power Platform connector authentication flow. The same documentation says custom connectors, MCP servers, and REST API tools do not add API permissions to the Entra Agent ID in the described manner. Validate each of those paths separately instead of generalizing the native-connector behavior.
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Salesforce Agentforce example
Salesforce states that its AI agents respect standard Salesforce controls, including licenses, permissions, field-level security, and sharing settings. Access to a custom action depends on the underlying Apex class, flow, or prompt template, so inspect that implementation rather than relying on the action label. The product statement is documented in Salesforce Help’s Trust and Agentforce guidance; treat it as a vendor description, not an independent security audit.
Salesforce also documents Agentforce agents and Prompt Builder templates as MCP tools for external assistants. Its MCP guidance says execution runs in the authenticated user’s context, with sharing rules, field-level security, and Einstein Trust Layer policies applying. This path requires explicit configuration, supports only agents created with the new Agent Script Builder, and requires legacy agents to be upgraded. The implementation details are in Salesforce’s Agentforce MCP tools documentation.
Evaluate governance as a lifecycle
Creation and publication
Define who may create agents, add tools, create connections, and move an agent from development to production. Require a named owner, data classification, intended audience, and rollback contact before publication. Separate the person who builds an agent from the person who approves a high-impact action when your control framework requires it.
Sharing and user eligibility
Check whether administrators can restrict an agent to groups, environments, or channels. Confirm what happens when a user changes roles, leaves the organization, or loses a source-system license. Test that shared links and copied configurations do not silently expand the audience.
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Identify the events available for agent invocation, tool call, policy decision, data access, error, and administrative change. Verify timestamps, user and agent identity, target system, operation, result, and correlation ID. Establish retention and export requirements before relying on the platform for incident response.
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Credential rotation and revocation
Document how to rotate client secrets, refresh tokens, certificates, and service-account keys without downtime. Test emergency revocation: disable the connection, remove the source permission, and delete or unpublish the agent. Confirm how quickly cached tokens, queued jobs, and downstream sessions stop working.
Telemetry and provider charges
Observability may require enabling provider telemetry or querying a provider’s logging or data services. Ask what data is collected, where it is stored, how long it is retained, and whether the provider may charge for those queries. Include those operational costs in the ownership model even when the agent platform itself has no separate telemetry line item.
Consider a cross-platform administration layer carefully
Microsoft’s Connected platforms documentation for Agent 365 describes connections to supported external agent platforms and synchronization of supported agents into the Microsoft 365 agent registry. The listed capabilities vary by provider:
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| Amazon Bedrock | Supported | Supported | Capabilities depend on the connected platform and configuration. |
| Google Vertex AI | Supported | Supported | Capabilities depend on the connected platform and configuration. |
| Salesforce Agentforce | Supported | Coming soon | Observability is not presented as generally available on that page. |
Connection setup is shared between administrators: the Agent 365 administrator manages the connection, while the source-platform administrator creates credentials, grants source permissions, and enables platform telemetry when applicable. Microsoft warns that collecting telemetry may query provider logging or data services and may incur provider charges. Use this layer for inventory and the capabilities explicitly supported, not as proof of uniform policy enforcement across providers. See Microsoft’s Connected platforms documentation.
Run a structured evaluation
- Select representative tasks. Choose two or three workflows that include both a normal read and a consequential action. Write the exact data sources, fields, operations, user groups, and expected result.
- Build an integration map. For every step, record the connector or tool type, supported operation, acting identity, required scope, channel, and source-system owner.
- Ask for the enforcement model. Require a written answer identifying configuration-time permissions, runtime policy checks, source-system authorization, and channel-specific exceptions.
- Configure a non-production tenant. Use representative schemas and policies without exposing unnecessary production data. Create separate administrator, maker, ordinary-user, and denied-access accounts.
- Exercise positive and negative cases. Confirm that an authorized user can complete the task and that a lower-privilege user is blocked or receives only permitted fields. Include expired credentials, disabled connections, and malformed input.
- Inspect evidence. Match the agent transcript, tool-call record, source-system audit log, and policy decision by correlation ID. Check that the recorded identity is the one you intended.
- Test lifecycle operations. Rotate credentials, change a user’s group, unpublish the agent, block the connector, and delete the connection. Measure the time until access actually stops.
- Record residual risk and ownership. For every unsupported operation, preview feature, manual control, or unclear log, name the owner, mitigation, and review date. Obtain current licensing and pricing directly from the provider before purchase.
Use a decision matrix instead of a feature tally
Weight the scorecard according to the workflow. A regulated write action might assign most of its weight to identity, runtime enforcement, and auditability, while a low-risk internal search might prioritize coverage and administration effort.
| Criterion | Suggested evidence threshold | Decision signal |
|---|---|---|
| Required operation exists | Documented operation succeeds in the target tenant. | Pass only when the exact object, fields, and write behavior are confirmed. |
| Least-privilege identity | Token and audit log show the intended user or narrowly scoped service identity. | Fail if a maker’s broad access is silently reused for ordinary users. |
| Runtime denial | Denied-access test blocks the call and produces an attributable event. | Pass when both platform and source-system logs explain the decision. |
| Channel consistency | Positive and negative cases behave as designed in every planned channel. | Document exceptions rather than averaging them away. |
| Lifecycle control | Approvals, rotation, revocation, and deletion are executable by named administrators. | Fail if emergency shutdown depends on an individual maker. |
| Operational visibility | Failures, usage, policy blocks, and synchronization state are reviewable. | Pass only when retention and any telemetry charges are acceptable. |
Common selection mistakes and how to correct them
Mistake: choosing the longest connector catalog
Symptom: The required system appears in a marketplace list, but the needed write operation or object is absent.
Correction: Validate the operation, object, fields, authentication mode, limits, and maintenance status in a test tenant.
Mistake: assuming the agent uses the requester’s permissions
Symptom: A demo works for the maker but exposes data or actions differently for ordinary users.
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Correction: Inspect the token subject and run paired authorized and denied-access tests. If a service identity is unavoidable, narrow its source permissions and add explicit approval for sensitive actions.
Mistake: treating setup consent as runtime authorization
Symptom: Administrators see a granted scope, yet calls are blocked by a data policy, or calls succeed more broadly than expected.
Correction: Document configuration scopes and runtime policies as separate controls, then test both an allowed and a blocked request.
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Symptom: Teams, a custom web channel, and an API produce different identities or policy results.
Correction: Repeat the same test matrix per channel and record connector type, token flow, and Conditional Access behavior for each.
Mistake: overlooking the underlying custom action
Symptom: An action label appears restricted, but its Apex class, flow, prompt template, or REST endpoint has broader access.
Correction: Review the implementation’s permissions and data handling, not only the agent configuration screen.
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Symptom: A pilot depends on preview functionality, provider telemetry, or a license that is unavailable to the intended users.
Correction: Mark preview and “coming soon” features explicitly, obtain a current licensing quote, and price telemetry, storage, support, and administration.
Make the final choice
Shortlist platforms only after each one has passed the workflow, identity, runtime, channel, and lifecycle tests. Select the candidate that leaves the fewest unowned assumptions: every required operation is supported, the acting identity is deliberate, source permissions are preserved, policy decisions are visible, and administrators can revoke access quickly. Re-run the tests whenever a connector, channel, identity model, or major platform version changes.
For administrative controls over Microsoft 365 plug-ins and connections, see Microsoft’s guidance on governing Microsoft 365 Copilot plug-in access and connections. Use it as a control reference, then verify the equivalent controls in the platform and source systems you actually deploy.
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