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You do not have to assume an AI agent is trustworthy to deploy it responsibly. You need a surrounding system that lets people inspect what it did and which data it used, anticipate its behavior through explicit rules, and constrain, stop, or redirect its actions.
What control means in practice
Control is a property of the deployed workflow, not a judgment about whether a language model is inherently trustworthy. Operators need three capabilities:
- Visibility: Inspect the agent’s actual actions and the data it relied on, rather than relying only on its own account of what happened.
- Predictability: Make business rules, exceptions, and judgment calls explicit. An agent cannot reliably follow norms that exist only in employees’ heads.
- Intervention: Limit what the agent can do, set conditions for human review, and provide a working way to stop or redirect an action.
Chris Willis makes this case in a Built In opinion article published September 14, 2026, reviewed by Seth Wilson. His line, “Transparency, accountability and oversight live in the interface or they don’t live at all,” is an editorial recommendation: oversight has to be available where people make decisions, not merely promised in a policy document.
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Make evidence visible beside each recommendation
Consider an agent recommending that a customer’s refund be denied. A reviewer should be able to see the recommendation’s basis at the point of decision: the policy applied, the records read, and the agent’s actions. A concise explanation generated by the same agent is not a substitute for access to that underlying information.
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The interface should also distinguish governed information from improvised output and make clear whether the request falls within the agent’s defined competence or requires judgment beyond it. The person reviewing the result needs a clear, usable path to intervene—not just a button that records disagreement after the decision has already taken effect.
Decide which tasks need a person
Human oversight should be matched to the task. Willis proposes considering three factors: the cost of an error, how much the task depends on explicit rules versus tacit judgment, and whether an action can be reversed. This is a practical triage framework, not a validated scoring system.
| Factor | Lower need for review | Higher need for review |
|---|---|---|
| Cost of error | A mistake has limited consequences. | A mistake could cause substantial harm or attract regulatory scrutiny. |
| Nature of judgment | The task follows explicit, codified data or rules. | The task depends on ambiguous, tacit, or undocumented judgment. |
| Reversibility | The action is easy to undo. | The action is difficult or impossible to undo. |
Good candidates for bounded automation
Expense categorization, support-ticket routing, and matching invoices to purchase orders are examples of work that can be relatively explicit and lower-cost. Where errors are limited and reversible, an organization may decide that a task can proceed without case-by-case approval, while still preserving visibility and intervention controls.
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Keep people involved in judgment-heavy cases
Non-standard discounts, responses to angry customers, and matters a regulator might question are examples where tacit judgment or the consequences of an error raise the need for human handling. An agent can help surface information or draft a recommendation, but that does not mean it should make the final decision.
Turn informal expectations into operating rules
Before automating a workflow, identify the business norms and exceptions employees currently apply. Specify what the agent may decide, what evidence it must use, and which conditions require escalation. If a rule cannot be stated clearly enough for the workflow to apply consistently, the organization should not assume the model will infer it reliably.
Rules also need to define boundaries: which actions are permitted, which require approval, and what happens when available information is incomplete or the case falls outside the agent’s competence. These boundaries make behavior more predictable and give operators a basis for reviewing exceptions.
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Build a real intervention path
For each consequential workflow, decide who can pause or redirect the agent and how they do it. Make sure the intervention happens before an action becomes difficult to reverse. Reviewers should be able to inspect relevant records, decline or change a recommendation, and route an out-of-scope case to a person with authority to resolve it.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why process and people matter alongside the model
Boston Consulting Group’s 2025 AI Radar publication describes a 10-20-70 principle followed by top-performing organizations: 10% of effort on algorithms, 20% on data and technology, and 70% on people, processes, and cultural transformation. BCG says the publication reflects 2025 survey data, with more than 1,800 executives participating. This is BCG’s description of an effort-allocation principle, not a universal measurement of how much value every organization gets from AI.
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The implication for deployment is straightforward: model capability alone does not establish control. Teams also need usable rules, operational ownership, and interfaces that expose evidence and support oversight.
What the guidance does—and does not—establish
Willis’s recommendations are editorial guidance, not results from empirical testing of the proposed task-triage framework. Organizations should treat the three factors as a way to structure decisions, then set review requirements appropriate to their own workflows, risks, and obligations.
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