To build an AI agent that automates a business workflow, first confirm that the process needs contextual judgment over unstructured inputs, or that its rules are too brittle to maintain as ordinary code. Then write a charter that names the goal, the boundaries, the tools and the escalation conditions. Build the narrowest version that can do the job, and place automatic checks and human approval in front of every action that changes a record or reaches a customer. Widen the agent’s autonomy only after testing and production monitoring show it behaves as specified.
The steps below apply across platforms. The examples draw on OpenAI’s developer tooling and Microsoft 365 because both publish official guidance on this subject. Neither vendor’s guidance establishes one universally best stack, so the platform choice should follow from the constraints described in each section.
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What makes a system an agent
An agent uses a model to pursue a goal. It chooses which tools to call, reads the results, and decides the next step until the task is finished. OpenAI’s practical guide to building agents puts it this way: “Agents are systems that independently accomplish tasks on your behalf.” A system that answers one prompt and stops is a chatbot, even when it runs on a capable model. The distinction matters for design, because once a system can call tools and cause effects, it needs the controls covered later in this guide.
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Decide whether a workflow merits an agent
Agents earn their place where a process needs contextual judgment, where inputs arrive as free text or documents rather than fixed fields, or where the rule set is so large, shifting or tangled that it is hard to express and maintain as code. OpenAI’s guide cites refund decisions, vendor security reviews and insurance-claim documents as examples of this kind of work. A stable checklist operating on structured inputs usually belongs in deterministic automation, which is easier to test and audit. If conventional rules or software handle a process reliably, adding a model only adds variability.
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| Question | Deterministic automation fits when | An agent fits when |
|---|---|---|
| Inputs | Fields arrive in a fixed structure | Inputs are emails, documents, chat messages or other unstructured content |
| Decision logic | The steps form a stable checklist with few exceptions | Decisions need contextual judgment or weigh several exceptions |
| Rule maintenance | Rules can be written and tested as code without constant change | Rules are numerous, conflicting or hard to keep current as code |
| Output | The same input must always produce the same result | A reviewed draft or recommendation is acceptable before any action |
Score a candidate workflow against these four rows. If most point to the left column, build deterministic automation and stop there. If the mix is genuinely split, use the model only for the judgment step and keep the rest in code.
Write the first design before writing prompts
Microsoft’s process guidance for building agents makes the point directly: “Create governance artifacts that document agent boundaries and business alignment.” For a first agent, that artifact is a short charter. Answer each of the following in one or two sentences, then review the charter with the workflow owner.
- Business goal: the outcome the workflow should produce, stated in terms the owner can check.
- Boundary: the trigger that starts the workflow, the point where it ends, and the handoff it makes to a person or another system.
- Owner: the named person accountable for the workflow and for approving changes to its instructions or tools.
- Data: the systems and fields the agent may read.
- Permitted actions: which actions are read-only, and which may change a record, send a message or contact a customer.
- Prohibited actions: actions the agent must never take regardless of input, such as issuing a payment or altering a contract.
- Outputs: the fields, format and destination of what the agent produces.
- Escalation conditions: the situations in which the agent must stop and ask a person.
Keep the charter and the agent’s instructions under version control, and review changes the way you would review code. An instruction edit that quietly changes behavior is exactly what this practice prevents.
Choose an architecture in proportion to the job
Architecture choices mostly come down to how much of the agent loop your team wants to own. OpenAI’s documentation separates three starting points. They differ in control and in who carries deployment responsibility, not in whether one is inherently better.
| Starting point | What it provides | Trade-off |
|---|---|---|
| Managed Agents API (OpenAI) | Hosted agent infrastructure | Reduces runtime work; leaves less room to customize the loop |
| Agents SDK (OpenAI) | An application-controlled agent loop | Your application controls deployment and integration, and your team maintains them |
| Responses API (OpenAI) | Direct model work, or building an agent from scratch | Your team owns the agent loop itself and carries the most responsibility |
Microsoft describes the same trade-off between managed orchestration and code-first frameworks. Managed orchestration can accelerate deployment but constrains customization. Code-first frameworks give more control and bring engineering and maintenance work. Microsoft’s process guidance for building agents across an organization recommends approved orchestration patterns for this reason.
Start with one agent, or none
Begin with a single agent or a fully deterministic workflow. Add specialist agents only when tasks have genuinely distinct roles that one prompt and one toolset cannot serve well, and justify each specialist with a concrete requirement. Multi-agent designs add prompts, traces, coordination logic and review surfaces, and each of those becomes something to maintain.
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Manager-style orchestration versus handoffs
The Agents SDK documents two patterns. In manager-style orchestration, a primary agent keeps responsibility for the outcome and calls specialists as tools. In a handoff, the specialist becomes the active agent. Manager-style keeps one agent accountable for the end result, while a handoff transfers the work fully to the specialist. In either pattern, code-defined routing and structured outputs make the transitions between steps more predictable than leaving every transition to the model. The Agents SDK documentation on agent orchestration covers both patterns.
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Sort tools into two groups before writing any of them. Retrieval tools only read: a lookup in a CRM or transaction database, a document reader, a search. Action tools change something: updating a CRM record, sending a message, or handing a ticket to a person. OpenAI’s guide lists examples of both kinds, and the distinction determines where approval controls belong.
Give each tool one bounded operation
A tool should do one thing with a narrow set of parameters, and it should validate both its arguments and its results. For example, a status-update tool that accepts only the statuses your workflow defines and rejects anything else is safer than a generic tool that writes arbitrary fields. Match permissions to the tool as well: a read-only agent should hold read credentials only.
Require structured outputs where software depends on them
When a downstream system expects specific fields, request structured outputs and validate them before use. Free-form text is fine for a drafted customer reply that a person will read. It is a poor interface for a system that parses a decision and acts on it.
Keep business rules in code
Calculations and thresholds that define policy, such as a refund ceiling or a discount limit, belong in deterministic code that the tool enforces. The model can recommend a value, but the tool decides whether that value is allowed. Keep the number of branches decided by free-form model output as small as the task allows.
When the system has no API
If a system has no usable API, OpenAI describes computer-use interaction as one possible approach. It needs the clearest limits in the design: an explicit list of screens and actions the agent may touch, and dedicated testing. The agent is operating an interface that was not built for it, so errors are harder to predict than failed API calls.
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Put checks next to every side effect
OpenAI’s guardrails and human review guide frames the control model in one sentence: “Use guardrails for automatic checks and human review for approval decisions.” In practice this means three layers: automatic checks on what goes into the agent, automatic checks on what comes out, and checks attached to each tool call that can change something.
The last layer matters most. In a manager or handoff design, a check on the primary agent may not cover a custom tool call made by a specialist. Attach validation to every tool that can create an effect, not only to the agent’s final answer.
Pause before consequential changes
Anthropic’s framework for developing safe and trustworthy agents, published 4 August 2025, uses an expense agent to show where approval belongs: before cancelling subscriptions or changing service tiers, the agent should seek approval. Apply the same logic to any action that cancels, edits or commits money or obligations.
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- The agent drafts the proposed action and states its reason in plain language, citing the records it used.
- The tool-level check validates the arguments against the charter’s rules before anything is queued.
- The run pauses. The reviewer sees the proposed action, the reason and the underlying data.
- The reviewer approves or rejects. A rejection returns to the agent with a recorded reason or routes to the escalation path.
- On approval, the tool executes and the outcome is logged. If no decision arrives, the workflow follows its escalation condition rather than retrying the action.
Keep the agent stoppable as well, so that a named operator can halt new runs and pending approvals without redeploying the system.
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Consider a refund-request workflow, which OpenAI’s guide cites as a candidate for an agent. A first version can stay deliberately small:
- Trigger: a customer message arrives in the support queue with an order number.
- Read tools: an order lookup and the refund policy document.
- Agent output: a recommended decision, the policy clause it relies on, and the order fields it checked.
- Deterministic logic: the refund ceiling and eligibility window are enforced in code, and the tool rejects any recommendation above the ceiling.
- Action gate: issuing the refund requires reviewer approval, so the agent may only queue it.
- Escalation: a missing order, conflicting records, or a customer who disputes the policy goes to a person.
The model handles the reading and the explanation. Code and the reviewer control the money.
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Test before you give it more autonomy
Build a small evaluation set before go-live. Include:
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- Edge cases and ambiguous inputs.
- Missing data and incomplete records.
- Tool errors and timeouts.
- Requests outside the charter, which the agent should refuse or escalate.
For each case, check whether the agent selected the right tool, respected its boundaries, produced valid output, stopped when uncertain, and escalated at the correct point. During development, run consequential actions only against sandbox or non-production connections.
Roll out with human review on every consequential action first. Review failures and approval decisions regularly, then tighten instructions, tool definitions or validations wherever the observed results show a gap. Extend autonomy only to action types that have a clean record under review.
Operate it: monitoring, failures and long waits
What to log
- Each tool call, with its arguments and results.
- Every approval, rejection and escalation, with the reviewer’s stated reason.
- Validation failures, grouped by check.
- Runs that stopped on uncertainty, so you can judge whether the escalation thresholds are set correctly.
Rerun the evaluation set after any change to instructions or tools, rather than judging the change by incidents alone.
Durable execution for waits and restarts
Work that waits on a person, retries after errors or spans process restarts needs state that survives those events. OpenAI’s SDK documentation describes integrations with Temporal and Dapr for long-running workflows, as covered in its guide to running agents. Treat these as implementation options rather than a requirement. A short approval step may not need them; an approval queue that stays open for days probably will.
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Platform availability and how to choose
Microsoft Copilot Workflows
Microsoft’s support page on getting started with Workflows in Microsoft Copilot, last updated in April 2026, describes a way to create workflows from natural language for supported Microsoft 365 services, including Outlook, SharePoint, Teams and Planner. Workflows can start from a schedule or an event, and the page describes visual testing and management. Access is in Frontier early access, initially in select markets and languages, and features may change. Confirm current availability for your market and language before planning a rollout around it.
Comparing options
Compare platforms on the same axes:
- Managed versus code-first deployment
- Customization versus engineering effort
- Orchestration control
- Connector and data compatibility
- State persistence and long-running execution
- Permission and approval controls
- Observability and evaluation support
- Hosting and data-governance requirements
- Current access, pricing and support
This guide does not compare prices. Pricing, access terms and support levels change, so read each vendor’s current terms directly before costing an option.
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