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What counts as verified completion?
Verified completion means the workflow meets explicit acceptance criteria and leaves evidence a responsible person or system can inspect. For a generated summary, that may mean checking its material claims against source documents. For an agent that updates a record, it means confirming the intended state in the system of record, rather than relying on the agent’s status message.
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This is an operational design principle, not a claim that one formal standard governs every AI task. The UK Government’s AI Playbook reproduces ISO 9000:2015’s definition of validation as “the confirmation, through the provision of objective evidence, that the requirements for a specific intended use or application have been fulfilled.” Applied to AI, the key is to state the intended use and gather evidence that addresses it.
Design the workflow before automating it
1. Map the work and its decision points
Break the process into steps and mark where AI will draft, extract, classify, recommend, or take action. Separate routine operations from decisions requiring judgment. A workflow that drafts a reply has different consequences from one that sends it, changes a customer record, or triggers a payment.
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For each task, document what the AI does, what people do, who owns the outcome, and who can pause, escalate, or stop the process. The Australian National AI Centre advises teams to be clear about what AI and people will do and how outputs will be checked in its workflow redesign guidance. Microsoft’s guidance makes the accountability point directly: “Delegating work to AI doesn’t transfer accountability.”
2. Write the completion test first
Before configuring the AI, write down the required output or system state, the acceptance criteria, the evidence needed to verify them, and the conditions that count as failure or an exception. Make each criterion observable. “Produce a useful report” is difficult to verify; “include the requested fields, link each material claim to a source, and flag missing data” gives a reviewer something concrete to check.
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- For text or analysis: check important factual claims against source material or a curated reference set; record corrections and unresolved claims.
- For actions: confirm the resulting record or state in the system of record when possible, and retain a trace of the action and its inputs.
- For incomplete or ambiguous requests: define when the workflow must ask for more information, route the item to a person, or stop without acting.
A model’s own statement that it completed a task is not independent evidence that the requested outcome occurred. Review is more reliable when the person can see the relevant inputs, sources, output, and resulting system state.
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There is no single review pattern for every workflow. Consider the impact of an error, whether it is detectable, how repeatable the task is, time pressure, reversibility, and volume. Microsoft identifies repeatability, impact, error detectability, and time sensitivity as useful factors when deciding whether to delegate work.
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The Australian National AI Centre describes four patterns for business workflows. Treat them as options to adapt to the use case, not universal thresholds:
| Pattern | Example fit | Design implication |
|---|---|---|
| Check before acting | High business impact | Require an authorized person to approve the output before it is used or the action proceeds. |
| Watch while acting | Medium impact and high volume | Allow routine actions to continue while making monitoring and intervention possible. |
| Spot-check | Low impact and very high volume | Specify a sampling approach and what happens when a check finds a failure. |
| Exception handling | Routine cases with predictable edge cases | Bound the cases AI can handle and route named exceptions to a person. |
Sampling is not a substitute for approval where an individual action could cause serious or hard-to-reverse harm. Likewise, requiring review of every low-impact item can be impractical; choose a control that makes failures visible and gives someone authority to respond.
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4. Set the approval gate and the person’s authority
For an agent, specify the exact point where it pauses, what evidence the reviewer sees, who is authorized to approve, and how the workflow resumes, escalates, or stops. For consequential or irreversible actions, approval rules should be set by the system’s designers or operators—not left to the agent to decide. Google Cloud’s agentic AI design-pattern guidance discusses human-in-the-loop checkpoints; the Canadian Centre for Cyber Security’s guidance on adopting agentic AI likewise emphasizes that outputs must be valid and reflect intended behaviour.
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5. Instrument, record, and improve
Keep a usable record connecting important outputs or actions to the relevant evidence. Document roles, decision rules, acceptance criteria, escalation routes, and technical or data dependencies. Track accuracy, quality, speed, overrides, exceptions, and user feedback; review those signals and revise or stop the workflow when performance or risk warrants it. No single score proves that every AI workflow is safe or complete.
For testing, NIST describes evaluation probes for agentic AI that “act as adversarial verifiers” in its Building Evaluation Probes into Agentic AI project. The practical point is to test whether the workflow meets its intended requirements, including how it behaves when inputs are incomplete or edge cases arise—not just whether it succeeds on ordinary examples.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Where human oversight is a legal or operational requirement
Requirements depend on the system’s use, legal classification, and jurisdiction. The European Commission’s AI Act Article 14 page describes human-oversight requirements for high-risk AI systems, including the ability to monitor operation, understand relevant capabilities and limitations, detect anomalies, interpret outputs, and remain alert to automation bias. The page states that its displayed text reflects a consolidated version as at 27 July 2026. This does not mean every AI workflow is legally classified as high-risk.
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The UK Government’s Data and AI Ethics Framework advises assigning oversight roles, verifying language-model outputs for incorrect information and potential bias, and preserving opportunities for human intervention. In engineering contexts, UK Home Office guidance on using AI says AI-assisted outputs should be reviewed and approved by suitably qualified and experienced people before production. The UK AI Cyber Security Code of Practice also recommends maintaining oversight capability and making outputs assessable to those responsible for them: Code of Practice for the Cyber Security of AI.
These sources address different contexts. Check the rules that apply to your system and use case rather than treating one jurisdiction’s guidance as a universal legal requirement.
Quick Recap
A practical completion checklist
- Is the intended outcome stated as a result someone can verify?
- Are acceptance criteria and failure conditions defined before the workflow runs?
- Can a reviewer inspect the sources, inputs, output, and relevant system state?
- Is the oversight pattern proportionate to error impact, detectability, volume, and reversibility?
- Are approval authority, escalation, pause, and stop paths explicit?
- Can the team trace important actions to evidence and learn from exceptions or overrides?
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