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To build an AI agent for a business workflow, start with one existing process where judgment, unstructured input, or hard-to-maintain rules make ordinary automation difficult. Limit the first version to one bounded job, connect it only to the tools that job needs, and put an automatic check or a human approval in front of every action that changes a record or reaches a customer. Widen its autonomy only after logged behavior shows it works as designed.
What makes a system an agent
An agent uses a model to decide how a workflow moves forward. It reads the current situation, chooses among available tools, takes actions or requests them, and keeps working toward a goal across several steps. OpenAI’s practical guide to building agents describes agents as “systems that independently accomplish tasks on your behalf.” That guide does not show a publication date.
A chatbot that answers one question in one turn is not necessarily an agent, even when a large model powers it. The distinction matters for risk. Once a system can call tools, a wrong decision can become a changed record, a sent message, or a closed ticket, so the design work shifts from answer quality to boundaries, permissions, and review.
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Decide whether the workflow needs an agent
An agent is worth its added complexity when a process needs contextual judgment, reads unstructured inputs such as emails, PDFs, or chat messages, or relies on rules that are numerous, inconsistent, or costly to keep correct in code. OpenAI’s guide uses refund decisions, vendor security reviews, and insurance-claim documents as examples of this kind of work.
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A stable checklist with fixed fields and known branches usually belongs in deterministic automation. It is easier to test, cheaper to audit, and does not need a model at all. If conventional software already handles the process reliably, do not add an agent simply to label the project as AI.
| Workflow trait | Points toward an agent | Points toward deterministic automation |
|---|---|---|
| Inputs | Free-form emails, scanned documents, or chat messages that need interpretation | Structured form fields or database records with a fixed schema |
| Decision logic | Decisions weigh context, and exceptions are common and varied | Fixed thresholds or a checklist with known branches |
| Rule maintenance | The rule set is large, contradictory, or changes often, making code brittle | Rules are few, stable, and documented |
| Exception handling | Exceptions need reasoning about the specific case | Exceptions can be listed and routed to a queue |
| Cost of a wrong action | Proposed actions can be reviewed before they take effect | Critical logic must be exact every time, so it stays in deterministic code |
Most production designs combine the two. The agent interprets a messy request and classifies it, while ordinary code applies approval thresholds and writes the final record. Microsoft’s process guidance for building agents likewise recommends deterministic workflows for critical business logic (Microsoft Learn, “Process to build agents across your organization with Microsoft Foundry and Copilot Studio”).
Map the workflow before writing prompts
Start from one existing process and document five things. Keep the first version narrow: one trigger, one outcome, and the exception paths you can actually test.
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- Inputs. Every document, record, and field the current person examines, and which of them are structured.
- Expected result. The output that counts as done: a decision, a drafted reply, an updated record, or a routed case.
- Exception paths. Cases where the normal route fails, including missing data, conflicting documents, and disputes.
- Current manual decisions. Each point where a person decides, the information they use, and what they do when unsure.
The last two items are where both value and risk concentrate. If staff rely on judgment that nobody has written down, that judgment must become explicit guidance and worked examples before the agent can be evaluated against it.
Write a charter that sets boundaries
Before selecting tools, write a charter that states the business objective and what the system must never do. Microsoft recommends documenting responsibilities, roles, and prohibited actions in an agent charter. Its guidance says to “Create governance artifacts that document agent boundaries and business alignment” (Microsoft Learn process guidance; the page does not show a publication date). A practical charter covers:
- The business objective and the measure that shows it is working
- The named owner of the workflow and the approver for changes to it
- The workflow boundary: where the agent starts, where it stops, and what it hands off to a person
- The data it may read, by system and field category
- Each tool, labeled read-only or state-changing
- Prohibited actions, for example issuing a refund above a set limit or contacting a customer without approval
- Stop conditions and escalation paths, including what the agent does when it is uncertain
Keep the charter and the agent’s instructions in version control or another change-controlled system. Treat any instruction change that alters what the agent may do as a release, with the same review as a code change. Informal edits to instructions can quietly widen what an agent does without anyone approving it.
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Choose an architecture in proportion to the task
OpenAI’s current API documentation presents three starting points for agents. They differ mainly in who runs the agent loop and who operates the runtime. Confirm the current names and features in OpenAI’s developer documentation before you build.
| Starting point | What it provides | Who runs the loop and deployment | Choose it when |
|---|---|---|---|
| Managed Agents API | Hosted agent infrastructure | The platform handles more of the runtime; your team does less runtime work | You want less operational work and can accept the platform’s constraints |
| Agents SDK | Application-controlled agent loops | Your application controls deployment and integration | You need code-level control over tools, routing, and deployment |
| Responses API | Direct model work, or building an agent from scratch | Your team owns the agent loop and its responsibilities | You need full control and can carry the engineering and maintenance load |
Microsoft draws a similar line between managed orchestration and code-first frameworks. Managed orchestration can accelerate deployment but constrain customization. A code-first framework gives more control and brings engineering and maintenance work with it.
Start with one agent or a deterministic flow
Begin with a single agent, or with no agent for the deterministic parts. Add specialist agents only when tasks have genuinely distinct roles, such as a step that needs a different toolset and a different set of instructions. Each additional agent adds prompts, traces, coordination points, and review surfaces, so each one needs a concrete requirement that a single agent cannot meet.
Compare manager and handoff patterns
| Pattern | What happens | Implication for controls |
|---|---|---|
| Manager-style orchestration | A primary agent keeps responsibility and calls specialist agents as tools | The primary agent stays accountable for the outcome, but an agent-level check may not cover every custom tool call a specialist makes |
| Handoff | The specialist becomes the active agent for the task | Responsibility moves to the specialist, so each specialist needs its own boundary and escalation rules |
Whichever pattern you use, attach validation at each tool boundary that can create an effect. Code-defined routing and structured outputs make workflow steps more predictable than leaving every branch to free-form model choice.
Where Microsoft 365 Copilot Workflows fits
Microsoft’s support page “Get started with Workflows in Microsoft Copilot,” last updated April 2026, describes a natural-language agent that creates workflows for supported Microsoft 365 services, including Outlook, SharePoint, Teams, and Planner (Microsoft Support). Workflows can run on scheduled or event triggers, and the page describes visual testing and management. Access is in Frontier early access, initially in select markets and languages, and the page says features may change. If your tenant or region is not covered, confirm availability with Microsoft before planning around it.
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Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →| Decision axis | Microsoft Copilot Workflows | OpenAI developer approaches |
|---|---|---|
| Build method | A natural-language description creates the workflow | Developers build agents through the managed Agents API, the Agents SDK, or the Responses API |
| Systems it reaches | Supported Microsoft 365 services named on the support page: Outlook, SharePoint, Teams, and Planner | Whatever tools your team defines and connects; OpenAI’s guide cites CRM and transaction databases, documents, and handing a ticket to a person |
| Triggers | Scheduled or event triggers | Not stated in the cited OpenAI pages |
| Access status | Frontier early access, select markets and languages, features may change (per the April 2026 update) | Check OpenAI’s developer documentation for the current status of each option |
| Pricing | Not stated in the cited Microsoft page; check current Microsoft terms | Not stated in the cited OpenAI pages; check current OpenAI terms |
The sources do not establish one stack as best for every team. Choose based on which systems the workflow must reach, how much control you need over the loop, and where your data is allowed to run.
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Connect tools with the least access the task needs
Separate retrieval tools from action tools. Retrieval tools read information without changing it. Action tools change something in another system. The distinction determines which controls each tool needs.
| Tool class | Examples from OpenAI’s guide | Minimum controls |
|---|---|---|
| Retrieval | Reading a CRM or transaction database; reading documents; searching | Read-only credentials; field-level scope where the system supports it |
| Action | Updating a CRM record; sending a message; handing a ticket to a person | Argument validation, a pre-execution check, an approval gate for sensitive changes, and a sandbox during development |
Design each tool around one bounded operation rather than a general-purpose “update anything” function. Validate the arguments the model supplies before execution, and validate results before the agent relies on them. When downstream software depends on specific fields, require structured outputs and validate them before use.
When the system has no API
OpenAI’s guide describes computer-use interaction as a possible approach for systems without an API. It needs the tightest limits of any integration: define exactly which screens and actions are allowed, test on representative and unusual screens, and require human approval before anything submits or changes data.
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Build validation and approval into each side effect
Use three layers. Automatic input checks run before the agent processes a request. Output checks run before a result reaches a person or another system. Tool-level checks run immediately around every call that changes state.
OpenAI’s guardrails documentation states the division of labor directly: “Use guardrails for automatic checks and human review for approval decisions” (OpenAI, Guardrails and human review). Anthropic’s framework for developing safe and trustworthy agents, published August 4, 2025, makes the same point with an example: an expense agent should seek approval before cancelling subscriptions or changing service tiers (Anthropic, “Our framework for developing safe and trustworthy agents”).
Design the pause, decision, and resume path
- Pause before the side effect. The agent stops before the call and stores the proposed action: the target record, the exact change, the inputs used, and the reason.
- Show the reviewer a plain-language proposal. The reviewer should see what will happen, to which record or customer, and why, without reading the full transcript.
- Record the decision. Approve or reject, with the reviewer’s identity and a timestamp.
- Re-check the target before executing an approval. The record may have changed since the proposal. Compare its current state with the state the agent saw, and stop if they differ.
- Route rejections. Log the reason and send the case according to the escalation rules in the charter.
Keep a stop control that halts the agent at any step. Design tool calls so that an interrupted run can be identified and reconciled against the systems it touched.
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Test representative and edge cases before live actions
Build a small evaluation set before connecting live systems. Include:
- Normal requests that represent most of the expected volume
- Edge cases and rare exception paths from your process map
- Ambiguous inputs where two readings are plausible
- Missing data, such as a document without a required field
- Tool errors, including timeouts and malformed responses
- Requests outside the charter, which the agent should refuse or route
For each case, check whether the agent selected the right tool, respected the boundaries, produced valid output, stopped when uncertain, and escalated at the right point. Run consequential actions in a sandbox or non-production connection during development. In the first production phase, keep human review on every action that changes data or reaches a customer.
Handle waits, retries, and failures
Workflows that wait for a person, retry after errors, or span process restarts need state that survives those events. OpenAI’s Agents SDK documentation on running agents describes integrations including Temporal and Dapr for long-running workflows (OpenAI Agents SDK, “Running agents”). These are implementation options, not requirements. A small workflow with a low approval volume may be handled with a queue in an existing database.
| Symptom | Likely cause to check | Response |
|---|---|---|
| The agent calls the wrong tool | Tool descriptions overlap or are too broad | Narrow each description, split broad tools, and add the failing case to the evaluation set |
| Downstream software rejects the output | Output is unstructured or not validated | Require structured outputs and validate them before use |
| The agent proceeds with missing data | The charter has no stop condition for that case | Add an explicit stop-and-escalate rule and a missing-data test |
| Reviewers approve proposals without clear reasoning | Proposals are long or do not state the target and reason | Shorten each proposal to the action, the target, and the reason |
| Runs stall waiting for a person | No timeout or resume path | Define a timeout, a default escalation, and a durable resume point |
Monitor behavior and widen autonomy in steps
Log every tool call with its arguments and result, every proposed side effect, every approval and rejection, and every escalation. These logs are the evidence for any decision to reduce review. Review them on a regular schedule with the process owner, and look for three patterns: routine cases sent for review, actions taken without review where the charter requires it, and the same proposal type being overridden repeatedly.
Change one thing at a time, whether instructions, tool descriptions, validation rules, or approval thresholds, and re-run the evaluation set after each change. Reduce approval gates only for action types whose logs show consistent correct behavior. Keep approval in place for any action the charter marks as sensitive.
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