Choose an AI agent platform by starting with the workplace process—not a vendor feature list. Define what the process must do, which data and systems it may touch, what actions it can take, and where a person must approve or take over. Then compare managed and code-first approaches against those requirements and pilot the leading option using real cases, including failures and edge cases.
Start by deciding whether the process needs an agent
A platform decision comes after a process decision. For stable work with explicit steps, a conventional function or workflow is usually simpler to control and maintain. An agent is a better fit when work is open-ended, requires interpreting varied inputs, or needs planning and tool use across multiple steps. Microsoft’s Agent Framework documentation puts the distinction plainly: “If you can write a function to handle the task, do that instead of using an AI agent.”
Use a workflow when the steps are predictable
Examples include routing a form according to fixed fields, applying a known calculation, or moving a record through a defined approval sequence. If a process can be described as rules and explicit transitions, adding an agent may introduce variability without solving a real problem.
Consider an agent when the work requires judgment and tool use
An agent may help when the input is unstructured, the next step depends on context, or the task requires gathering information from approved systems and producing a tailored response. That does not mean it should act without limits: autonomy should be bounded by permitted data, tools, and actions.
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Write the process brief before comparing platforms
Describe one real process in terms that business, IT, security, and compliance owners can review. Microsoft’s governance guidance recommends defining an agent’s boundaries and business alignment before implementation.
- Outcome: What business result should the process achieve, and how will you recognize a successful completion?
- Trigger and inputs: What starts the work, and what records, documents, messages, or other information does it receive?
- Systems and data: Which repositories, applications, APIs, and identity systems must it use?
- Allowed actions: What may it read, create, update, send, or approve?
- Prohibited actions: What must it never do, even if a user request or input appears to ask for it?
- Owner and escalation: Who owns the process, and what should happen when information is missing, confidence is low, or an action falls outside the defined boundary?
This brief is the basis for the platform requirements. If a candidate cannot support the required systems, identity model, review points, or operating controls, its feature count is beside the point.
Choose the operating model that fits your team
Managed orchestration and code-first frameworks trade setup effort against control and engineering responsibility. Microsoft’s guidance describes the broad trade-off as faster deployment and built-in security for managed orchestration, versus greater customization and multicloud flexibility for code-first frameworks, which require significant engineering investment and maintenance.
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| Approach | Best fit | Main advantage | Main trade-off |
|---|---|---|---|
| Managed orchestration | Teams prioritizing deployment speed and a managed operating environment | Can accelerate deployment and provide built-in security | May limit customization |
| Code-first framework | Teams with engineering capacity that need more customization or multicloud flexibility | Greater control over implementation | Requires substantial engineering investment and ongoing maintenance |
Do not treat either approach as inherently safer or more capable for every workflow. Confirm that the specific platform and configuration meet your process, security, and operational needs.
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Inventory the data sources and connections the process needs before evaluating demos. For each repository, business application, API, and identity system, establish whether the platform can connect in an approved way and whether it can restrict access to the data and actions the process actually requires. Microsoft recommends using governed repositories and least-privilege access.
- Identify required connectors and APIs, including any system where the agent must write or trigger an action.
- Check how data is governed, filtered, and kept within the intended boundary.
- Determine whether access can follow the user’s identity or must use a service identity, and how that identity is constrained.
- Confirm that integrations expose only the operations needed for the process rather than broad, unnecessary permissions.
- Ask how connections, credentials, and changes to integrations are managed over time.
The 2025 MIT AI Agent Index reports that 20 of 30 agents in its study sample support Model Context Protocol (MCP) for tool integration. The index cautions that proprietary connectors are often promoted over open MCP servers. This is a finding about the studied sample, not a market-wide adoption estimate; verify the connectors and integration path your workflow actually requires.
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Set action controls before a platform demo
Decide in advance which actions require a person. Human confirmation is especially important when an action changes records, sends an external message, commits money, or affects access. Microsoft explicitly recommends human confirmation for high-impact actions and isolated testing before production.
Define the approval boundary
Specify which actions an agent may complete independently and which must pause for review. Define who can approve, what information that person sees, and what happens if approval is denied or no reviewer is available.
Limit and validate tool use
Check whether permissions can be narrowly scoped, inputs validated, and actions constrained to the intended systems and records. Test how the platform handles malformed, ambiguous, or adversarial input rather than assuming a tool connection is safe because it is available.
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Keep an operational record and a way to intervene
Confirm what execution details are logged, whether actions can be attributed to a user or service identity, and whether operators can pause or stop a run. Also establish an incident-handling route for unexpected actions or failures. The required level of trace detail depends on the process’s support, audit, and compliance needs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Run a representative pilot, not a polished demo
Use cases drawn from the actual process and production configuration. Include routine requests, incomplete or ambiguous inputs, adversarial attempts to exceed the agent’s authority, and system or tool failures. A platform that succeeds only on a clean demonstration case has not shown that it is ready for workplace automation.
- Prepare the cases: Include expected outcomes and identify which errors would be low-impact versus unacceptable.
- Run each case: Record whether the task completed, needed escalation, or took an incorrect action.
- Review the execution: Inspect the available trace, tool calls, approvals, and action attribution to see whether an operator could understand and investigate the run.
- Measure operational fit: Track task completion, error severity, latency, cost at expected volume, and escalation rate.
- Test recovery: Check how operators pause or stop execution, handle an incident, and return the process to a safe state.
- Repeat after configuration changes: Re-test the exact permissions, integrations, and approval settings intended for production.
The MIT AI Agent Index’s 2025 sample illustrates why traceability deserves explicit testing: it reports detailed action traces for 10 of 30 agents studied; 6 of 30 show summarized reasoning without detailed tool traces, and the paper says monitoring of individual executions is unclear for many enterprise agents. These counts describe the index’s sample, not every current platform. Verify observability in the product and configuration under consideration.
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Compare finalists on the criteria that affect your process
Once a process brief and pilot have narrowed the field, compare the remaining options against the same criteria. Weight the criteria according to the impact of errors and the team’s operating constraints, rather than assuming every category matters equally.
| Criterion | What to verify |
|---|---|
| Task fit and autonomy | Whether the work is a fixed workflow, an agent-assisted workflow, or autonomous multi-step execution; how and when a person can intervene. |
| Integration and data fit | Required connectors and APIs, data governance and filtering, identity propagation, and the scope of write access. |
| Control and auditability | Permission scoping, approval gates, input validation, execution traces, monitoring, stop controls, and incident response. |
| Build and operate effort | Managed versus code-first fit, skills required, customization, environment lifecycle, and who owns maintenance. |
| Evaluation and economics | Task-specific quality and reliability, latency, quotas, usage visibility, and total cost at expected volume. |
| Portability | Model and tool choices, export options, and the engineering effort required to move or operate across environments. |
The reviewed sources establish that model and integration support differs among platforms, but do not provide a complete vendor-by-vendor portability assessment. Likewise, they do not establish current cross-vendor prices or contract terms. Treat portability and total cost as items to verify with each vendor for the intended deployment and expected usage.
Plan for production and change
A successful pilot is not the whole operating model. Before production, establish how the agent and its integrations will be reviewed, changed, monitored, and retired. Microsoft’s maturity guidance identifies environment separation, source control, review and approval flows, rollback, reusable integrations, monitoring, and cost allocation as features of mature platform practice.
- Separate development, test, and production environments where the platform supports them.
- Put agent definitions and related changes under source control and review.
- Use approval flows for production changes and define a rollback route.
- Assign owners for reusable integrations, ongoing monitoring, and incident response.
- Track usage and allocate costs so actual operating expense can be compared with expected volume.
- Re-evaluate the process when its systems, permissions, policies, or business requirements change.
Make the selection on evidence from your workflow
Shortlist platforms only after confirming that they can support the process’s data, systems, identity, and approval boundaries. Prefer the option that passes the representative pilot and can be operated responsibly by the team that will own it. There is no evidence here for a universal best platform: the right choice depends on the workflow, its risk, and the organization’s engineering and governance capacity.
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