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Design AI workflows one task at a time: automate repeatable, low-impact steps that people can verify, keep humans in charge of consequential or ambiguous decisions, and require approval before actions that affect people, money, or compliance. A review point only works when the reviewer can see what the AI proposes, understand the relevant context, and stop or correct the next action.
Decide what the AI may do at the task level
A workflow is a chain of tasks, not one indivisible unit. A system might safely sort routine requests, prepare a summary, and still need a person to resolve exceptions or authorize a commitment. Assess each task using four questions from Microsoft’s task-selection guidance. Treat them as a decision aid, not a numeric score or guarantee of safety.
- Repeatability: Does the task follow a familiar pattern, or does it require novel judgment?
- Impact: What could happen if the output or action is wrong?
- Error detectability: Can a reviewer check the result against source facts before it matters?
- Time sensitivity: Is there enough time for a meaningful review? If not, Microsoft advises keeping the task human-led.
Use those answers to choose an operating mode for each step:
The Tool Desk
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|---|---|---|
| Automate with review | Work is repeatable, relatively low-impact, and straightforward to verify, such as drafting an internal update or preparing a recurring summary. | Check the output before it is shared or used to trigger another action. |
| AI-supported, human-led | The AI can prepare analysis or options, but impact, subtle errors, or missing context make judgment important. | Make the decision and own its consequences. |
| Fully human-led | The task is highly variable, hard to verify, or too consequential for delegated execution. | Perform the task. AI may still help with bounded preparation, but does not own the decision. |
Do not let a task’s apparent convenience decide its automation level. Delegating work to AI does not transfer accountability, as Microsoft’s guidance puts it.
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Place review before consequential actions
Put a checkpoint at the transition where an AI output could affect someone or cause a difficult-to-reverse downstream effect. The checkpoint must come before the action, not after it has already been sent, committed, or applied.
- Before sending external communications or customer-facing proposals.
- Before budget approvals or other decisions that affect money.
- Before actions that are difficult to reverse or that affect people, commitments, or compliance.
- When a case is sensitive, ambiguous, exceptional, or missing necessary context.
- Before a recommendation, priority change, or decision is shared or used to trigger another action.
Microsoft’s responsible AI guidance recommends deciding during design which actions need human approval, especially hard-to-reverse actions and those affecting people, money, or compliance. Its checkpoint guidance also identifies exceptions, unclear priorities, and decisions affecting people or commitments as reasons to route work to a person.
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A “reviewed” label is not a control if the agent has already acted or the reviewer cannot intervene. Show what the system plans to do, provide the context needed to assess it, make approval responsibility explicit, and let an authorized person correct, interrupt, or reject the proposed action.
Set boundaries and reviewer context before building
Write down the workflow’s boundaries during design, before implementation makes them expensive to change. Microsoft’s planning guidance organizes this around Goal, Freshness, Context, Audience, and Checkpoints. Its example is an intake agent that sorts and routes standard requests while people handle judgment calls, unclear priorities, sensitive items, and decisions affecting commitments.
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- Goal: State the specific task and what the AI should leave out.
- Freshness: Identify which information sources are current and approved, and how stale information will be recognized.
- Context: Specify what the system and reviewer need to know. Define what happens when required context is missing.
- Audience: Identify who receives the output and whether it is suitable for them.
- Permitted actions: Separate what the system may recommend, pass along, or trigger from what requires a human decision.
- Checkpoints: Define what must stop for review, approval, or escalation, and who is responsible for each decision.
- Ownership: Name who maintains the system, receives incidents, and can pause or deactivate it.
Weak boundaries can let incomplete or stale information travel into downstream decisions. A reviewer also needs more than a final answer: provide the proposed action and relevant evidence or context so the person can exercise judgment rather than approve blindly.
Make approval a usable control, not a bottleneck or formality
For every checkpoint, define the decision the reviewer is being asked to make. A practical approval screen or handoff should make clear what will happen if approved, what information supports the proposal, and how to reject, edit, or escalate it. Keep sensitive and ambiguous cases on an obvious route to a person rather than asking the agent to resolve uncertainty silently.
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- Assign an accountable reviewer or role; do not leave approval ownership implicit.
- Give reviewers the source information and workflow context needed to verify the proposal.
- Expose planned and completed actions so reviewers can tell whether the system has already changed anything.
- Provide a way to interrupt autonomous behavior and a safe alternative process if the system is paused.
- Ensure the review window allows a meaningful decision; a rushed approval is not meaningful oversight.
There is no universal numeric threshold for when a person must approve, nor a single review frequency that fits every workflow. Set thresholds and routing rules according to the affected people, possible consequences, reversibility, and the evidence a reviewer can actually check.
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Review before launch, then monitor and intervene
Preproduction approval does not settle whether a workflow remains appropriate. Assess it before production, scale the assessment to risk, and revisit the design when the model, data, usage, workflow, or applicable requirements change. Microsoft’s responsible AI guidance and responsible AI policies describe design and prelaunch checkpoints, risk-based sign-offs, ongoing audits, and incident response.
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After launch, monitor for groundedness, safety issues, escalations, user-reported problems, and changes in the conditions the workflow depends on. Decide in advance who receives incidents, who can stop the system, and how work returns to a safe alternative process. NIST’s Generative Artificial Intelligence Profile (NIST AI 600-1) includes suggested actions for incident response and recovery, including disengaging or deactivating systems whose performance or outcomes conflict with intended use.
Use risk frameworks as guidance, not proof of safety
The NIST AI Risk Management Framework (AI RMF) is voluntary guidance intended to help organizations incorporate trustworthiness considerations into AI design, development, use, and evaluation. NIST released AI RMF 1.0 on January 26, 2023, and says the framework is being revised; check its page for current status when using it.
The AI RMF Playbook offers voluntary suggested actions and references organized around Govern, Map, Measure, and Manage. NIST announced its first complete version on March 30, 2023. The Generative AI Profile, NIST AI 600-1, was published July 26, 2024. These resources can organize risk work, but using a framework—or adding a review gate—does not by itself prove that a particular workflow is safe, lawful, or compliant.
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