An AI agent reviews an onboarding request, flags missing information, and recommends the next step. That can save your team time. But before its recommendation moves work forward, you need to answer a few practical questions.
What information did the agent receive? What did it return? Which actions could follow automatically? When was a person required to approve the next step? And what happens if you need to stop AI processing?
For regulated organizations, those questions belong in the workflow design. Clear records, approval gates, and defined stop controls make it possible to introduce AI with responsibilities and boundaries that people can understand.
If you already maintain an approval workflow audit trail, AI adds another participant to that record. You need to connect its contribution to the human decisions and workflow actions that follow.
What FINRA guidance contributes to the discussion
FINRA’s 2026 Annual Regulatory Oversight Report explains that existing obligations continue to apply when member firms use generative AI. It discusses formal review, testing, ongoing monitoring, and documentation. Potential monitoring practices include retaining prompt and output logs, tracking model versions, and reviewing outputs with human involvement.
For agents, FINRA highlights risks involving autonomy, authority, data sensitivity, and auditability. It invites firms to consider oversight, action tracking, and controls that restrict agent behavior.
These considerations provide a useful lens for workflow design. Their regulatory application depends on the firm and activity; they are not a universal product certification or a blanket requirement for a feature named “kill switch.”
Define the authority of each AI step
Start by specifying the job the agent is allowed to perform. Reviewing selected fields for missing information is a different responsibility from approving a customer, releasing a payment, or sending an external communication.
A useful configuration identifies the information the agent can receive, the instructions it should follow, where its results are stored, and which subsequent actions require approval. Assign an owner who can evaluate whether that configuration remains appropriate as the process changes.
HighGear’s Agentic AI Workflow Node lets administrators select the task fields shared with the agent, define its instructions, and choose the fields that receive its results. A dedicated permission controls who can configure and manage those nodes.
Those boundaries give teams a concrete starting point for governance: the workflow defines how an AI result is used.
Preserve the evidence behind AI assisted decisions
A final status rarely explains enough on its own. When an AI result influences work, the record should help a reviewer connect the information evaluated, the output returned, and the resulting decision.
Depending on the use case, useful evidence may include:
- The request and task information supplied to the agent.
- The instructions, response, execution time, and model information.
- The workflow actions triggered by the result.
- The person who approved or overrode a recommendation, including their reason.
- Relevant changes to instructions and workflow logic.
HighGear records AI request details, with full request details logged and encrypted within each HighGear instance. Currently accessible high-level information includes the AI provider and model used, credits consumed, and whether the request succeeded. Full request details are not currently available for reporting.
That distinction matters when designing an audit process. Confirm which evidence reviewers can access through existing interfaces and which details require a separate retrieval process. Establish retention and access procedures appropriate to the records involved; the existence of a log alone does not establish that every examination or reporting requirement has been met.
Put human approval before consequential actions
An approval gate gives a named person authority over a defined next step. For the gate to be useful, the reviewer needs enough context to assess the recommendation, the ability to reject or override it, and a clear record of the decision.
In HighGear, a workflow can hold an AI-generated recommendation for an authorized person’s approval before downstream actions occur. That person can override the recommendation and record a reason.
The placement of those gates should reflect the consequences of the action. A team might automate routine categorization while requiring review before a sensitive request advances. Define the conditions in the workflow, including what happens when the AI response is missing, unusable, or inconsistent with required information.
A prompt asking an agent to seek approval should be supported by a workflow step that actually enforces the approval requirement.
Know exactly what the stop control does
“Kill switch” is useful shorthand, but teams need a precise operational definition. Does the control prevent new requests? Cancel queued work? Interrupt an active request? What happens to tasks that arrive after the control is used?
HighGear provides a global Allow Agentic AI Nodes in Workflow setting. When disabled:
- Administrators cannot add agentic AI nodes to workflows.
- New executions of existing agentic AI nodes are prevented.
- Pending executions are stopped.
- Active requests are not interrupted.
- Tasks that subsequently reach an agentic AI node raise an error indicating that the feature is disabled.
This is a global disable control with defined limits. It does not cancel a request already in progress or automatically move affected tasks into a manual process.
Before relying on it, establish who may disable the feature, how affected tasks will be identified, and how the team will review work associated with active requests. Document the recovery procedure and test it before an incident makes that procedure urgent.
Govern AI assisted workflow changes
Governance also applies when AI helps build the process.
HighGear’s Workflow Assistant creates workflow drafts for administrators to review, refine, and publish. The AI Formula Assistant introduced in HighGear 10.4 helps administrators create and troubleshoot formulas using plain-English prompts. Administrators must accept and save its suggestions.
These capabilities support process design. The Agentic AI Workflow Node performs AI evaluation during workflow execution. Each needs review appropriate to its role.
For example, a formula that determines when additional approval is required should be tested against normal cases, exceptions, and threshold boundaries before use. An AI-generated suggestion still needs someone accountable for its effect on the process.
HighGear logs and versions changes to formulas, agent instructions, and workflow logic. The full change log is not currently visible to administrators. Teams should account for that visibility limit when establishing their review and change documentation procedures.
A practical onboarding example
Consider a hypothetical financial-services onboarding workflow. The following is an illustrative configuration, not a customer case study.
- Receive the request. A structured form collects the information needed for the onboarding process.
- Evaluate selected information. An AI node reviews administrator-selected fields and returns a summary of missing information or issues requiring attention.
- Apply workflow rules. Configured logic routes the request to the appropriate review step.
- Require approval. An authorized reviewer evaluates the AI recommendation and supporting information, then approves, rejects, or overrides it with a reason.
- Continue the process. The required approval allows downstream work to proceed, with the AI result and human decision connected to the task.
The team also defines how to handle AI request failures and how to recover affected tasks if agentic AI is disabled. Those procedures are part of making the workflow usable under real conditions.
Questions to answer before deployment
Before introducing an agent into a regulated workflow, confirm:
- What data can it receive, and who can change that scope?
- What can happen automatically after it returns a result?
- Which actions require human approval?
- Can reviewers override recommendations and document why?
- Which execution records are accessible, reportable, and retained?
- Who can stop AI processing, and what remains active afterward?
- How will failures and affected tasks be handled?
- How will changes to prompts, formulas, and workflow logic be reviewed?
Answering these questions gives the team a practical basis for deploying, monitoring, and improving the process.
Build AI into a process your team can govern
HighGear brings AI evaluation into configurable workflows with defined data boundaries, approval steps, and administrative controls. That gives teams a way to decide where automation can proceed and where human judgment remains necessary.
Book a consultation to explore how AI-assisted evaluation, approval gates, and audit records could fit your workflow.
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FAQs
What is AI agent governance in regulated workflows?
AI agent governance defines what an agent can access, what it can do, and when human approval is required. It also establishes how teams record AI activity, review changes, and stop processing when needed.
What should an AI workflow audit trail include?
An AI workflow audit trail should connect the information supplied to the agent, its instructions and response, and the actions that followed. Useful records may also include execution time, model information, human approvals, overrides, and configuration changes. Teams should confirm which records are accessible, reportable, and retained.
When should an AI recommendation require human approval?
Human approval should precede consequential actions, such as approving a customer, releasing a payment, or sending a sensitive external communication. The workflow should enforce the approval requirement and give reviewers enough context to approve, reject, or override the recommendation with a documented reason.
Does an AI kill switch stop every request immediately?
The effect depends on the control. In HighGear, disabling the global Allow Agentic AI Nodes in Workflow setting prevents new executions and stops pending executions. It does not interrupt active requests or automatically route affected tasks into a manual process. Teams need documented recovery procedures.
How does FINRA guidance relate to AI agent governance?
FINRA’s 2026 Annual Regulatory Oversight Report explains that existing obligations continue to apply when member firms use generative AI. It discusses review, testing, monitoring, documentation, and risks associated with agent autonomy and authority. Its application depends on the firm and activity; it does not establish a universal requirement for a feature called a “kill switch.”