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You can introduce AI agents into month-end close without surrendering auditability by limiting their authority, placing accountable review before accounting use, and retaining evidence that lets someone reconstruct each consequential run. Start with a bounded, checkable task; define the data and actions the agent may use; map controls to its output and changes; and make a named person responsible for reviewing the result. A framework helps organize that work, but does not make an agent or its output auditable by itself.
What auditability requires when an agent enters the close
For financial close, auditability is the practical ability to explain and support what happened: what information was used, what the system did with it, what it produced, who evaluated the result, what was changed, and when an accounting action was approved or completed. The record should let a qualified reviewer follow the work rather than rely on an unexplained output or a general assurance that the system is reliable.
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That matters because an agent can affect financial reporting through ordinary workflow actions as well as through model output. It may ingest documents, transform or compare information, orchestrate tasks, recommend a treatment, monitor a process, or interact with a person. Those capabilities have different control implications. Extracting invoice fields for review is not the same authority as changing accounting data, submitting a journal, or resolving an exception.
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This article uses “agent” in the practical close-workflow sense: a system that can work with supplied information and, depending on its setup, invoke tools or take workflow actions. The title does not imply that every product marketed as an agent has the same capabilities. Map the actual system behavior rather than relying on a product label.
Choose a task with a checkable result
Begin with a specific close activity whose inputs, expected output, and exception path can be described before deployment. A narrow task makes it easier to define what a correct result looks like and where a reviewer must intervene. Avoid starting with an open-ended mandate such as “complete the close”; it combines many activities, data sources, and judgments into a boundary that is difficult to control.
The IIA’s examples of agentic AI in internal audit include comparing invoices with purchase orders, checking signatures and approvals, marking up documentation, summarizing steps, and requesting information from business owners. These are examples of audit work, not evidence that a particular close agent performs those tasks reliably. They illustrate possible bounded activities, not a recommendation to delegate accounting judgment.
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| Capability in the workflow | What to define before use | Control question |
|---|---|---|
| Ingestion or transformation | Source documents, fields, permitted transformations, and destination for the result | Can the reviewer trace a result back to the source and identify missing or altered information? |
| Comparison, analysis, or recommendation | Comparison criteria, relevant policy, thresholds, and how the system presents uncertainty or exceptions | Can a reviewer evaluate the output against evidence and accounting policy rather than accept it as a conclusion? |
| Orchestration or monitoring | Tasks the system may initiate, recipients, escalation conditions, and completion evidence | Can the owner tell what was initiated, what failed, and which items remain unresolved? |
| Posting or other consequential action | Exact action, affected records, approval point, and accountable approver | Is there a control that prevents an unreviewed output from becoming an accounting record? |
The table is a design aid based on COSO’s categories, not a universal permission rule. Whether a capability is appropriate depends on the organization’s accounting policy, materiality, process design, and risk assessment. In particular, do not assume that posting or judgment must always be prohibited; decide explicitly what the system may do and what approval the organization requires.
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Map data, actions, and accountability before configuration
Write down the workflow from its source systems to its final destination. Include the data and documents the agent reads, transformations performed, tools or connected systems it may invoke, output locations, human decision points, and control owners. This map should describe the configured process, not merely the desired process. If the system can take an action through an integration, treat that as authority that needs an explicit boundary.
For each step, distinguish what the agent may read, draft, recommend, change, or submit. Those verbs are a practical authority scale: reading evidence has a different consequence from editing source data; drafting a journal differs from posting it. Set required approvals for consequential actions such as edits to source data, account coding, journal preparation, posting, close certification, and disposition of exceptions. The approval matrix should follow the organization’s policy and risk assessment; COSO provides a control framework and lifecycle guidance, not an organization-specific matrix.
Assign a human owner to each control, not simply to the overall AI initiative. The owner should know what the control is intended to prevent or detect, what evidence demonstrates it operated, how exceptions reach the right person, and who can authorize a change to the workflow. Separate the person configuring or maintaining the agent from the reviewer where the organization’s control design calls for that separation.
- Inputs: Identify approved sources, relevant periods, required documents, and who is responsible for completeness.
- Access: Limit connected systems and data to what the task needs; document the agent’s permitted tools and actions.
- Output: Specify the format, destination, and status of an output, such as draft, recommendation, or approved record.
- Decision rights: Identify who may accept a recommendation, correct it, approve an exception, or authorize a posting.
- Failure route: State what happens when data is missing, instructions conflict, evidence does not agree, or the system cannot complete the task.
Retain evidence that can reconstruct each consequential run
Design the evidence record before the agent enters a live close. For each consequential run, a reviewer should be able to determine which inputs and documents were used, which model or agent configuration was in effect, what tools or actions were invoked, what output and exceptions resulted, who reviewed the result, what was changed, and when approval or posting occurred. This is a practical control-design recommendation, not a log schema prescribed by an auditing standard.
Keep the source evidence alongside the relevant output or preserve a reliable way to retrieve it. Also retain the reviewer’s basis for accepting, rejecting, or correcting an output. A final approved number alone may not explain why it was accepted; an output alone may not show whether anyone reviewed it. Evidence should connect the inputs, agent activity, review, and accounting outcome without requiring a reviewer to infer missing steps.
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Define what a “run” means for the workflow. It might be one reconciliation, one batch, or another consequential unit of work. Whatever the unit, the record needs enough context to distinguish one execution from another and to associate the right inputs, configuration, review, and result. Retention arrangements should follow the organization’s existing accounting, control, and record-retention requirements; the available guidance does not set one universal retention period or exact technical format.
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IAASB’s Audit Documentation When Using Automated Tools and Techniques (2020) discusses documentation implications under ISA 230 and other relevant ISAs. It is non-authoritative support material and explicitly does not override the standards. It supports considering how automated tools affect documentation; it does not mandate the specific record fields described here.
Put a substantive review before accounting use
When an agent’s output may contribute to accounting records, require review appropriate to the task and risk before the output is used for accounting. An IIA-hosted guide, Internal Audit of Artificial Intelligence Applied to Business Processes (2025), specifically calls for identifying AI systems that can affect financial information and reviewing their outputs before accounting use.
A review should test the output against the underlying support and the relevant accounting policy. For a reconciliation, that can mean checking that the compared information belongs to the intended accounts and period, that relevant source evidence is present, that differences are accurately represented, and that exceptions were resolved through the approved process. For a proposed journal, the reviewer should evaluate the support, completeness, accuracy, and accounting treatment before any posting approval. These are practical review considerations; the exact steps depend on the task.
“Human in the loop” is not a sufficiently precise control description. A person who merely clicks approve without reviewing appropriate evidence may not provide meaningful review. Define the reviewer, the evidence the reviewer examines, when the review occurs, the method or threshold used, the path for unresolved exceptions, and how overrides or corrections are recorded. The sources do not establish a universal sample size, approval threshold, or review method.
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Keep review and accounting use as distinct steps in the workflow. A useful design is to route the agent’s result to a review state where it can be accepted, rejected, or corrected, and only then allow the approved result to move to an accounting record or consequential workflow action. The interface alone is not evidence that the control operated: retain a record of the reviewer’s decision and any resulting change.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Monitor performance, exceptions, and changes
Controls need to address operation after launch, not only initial setup. Assign an owner to monitor failed runs, overrides, unresolved exceptions, missing evidence, unexpected output changes, and updates to the model, prompts, configuration, connected data, or software. Define who investigates an issue, how it is escalated, and what action is taken if the process cannot be relied on for the current close.
When configuration or connected data changes, assess whether the change affects risk and test the controls relevant to the changed behavior. A revised instruction or tool access can alter what the agent does even when the task name remains the same. COSO identifies model drift and frequent configuration changes as risks and discusses controls across the GenAI lifecycle. Its 2026 materials include practical resources such as risk-assessment matrices, testing procedures, metric dashboards, and control mapping.
Use monitoring measures that connect to the process and its controls rather than treating activity volume as proof of success. For example, an owner can track whether runs fail, whether evidence is missing, whether reviewers frequently correct outputs, and whether exceptions remain open. Establish the organization’s own investigation criteria and escalation thresholds: the cited guidance does not supply universal numeric limits for close agents.
Involve internal and external audit with clear roles
Internal audit can assess governance and controls or provide bounded advisory input. The IIA’s Artificial Intelligence Auditing Framework (2024) provides a lens for evaluating governance, risks, data, algorithms, cybersecurity, transparency, traceability, and accountability. The framework describes use in audit planning and assurance or advisory services, and recognizes reasonable assurance in light of AI’s complexity and evolution.
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Define internal audit’s role early enough to provide useful perspective, while preserving its assurance independence. If internal audit advises on a design, clarify the advisory scope and who in management owns decisions and controls. Management remains responsible for the process and its control design; internal audit’s framework is not a substitute for that ownership.
External audit requirements continue to be governed by applicable standards and jurisdictional rules. IAASB’s 2020 automated-tools documentation resource is non-authoritative and does not change the ISAs. Separately, on August 5, 2026, IAASB published an exposure draft proposing changes to ISA 330, ISA 500, and ISA 520, including a stronger basis for assessing the relevance and reliability of audit evidence. That is a proposal, not an effective requirement. Do not design or describe a control as mandatory on the basis of the exposure draft.
A practical rollout sequence
- Select one bounded activity. State the close task, its expected result, the evidence needed to check that result, and which outcomes are consequential.
- Map the actual workflow. Trace data from source through transformation and output; record tools, integrations, human decisions, and record destinations.
- Set authority and control ownership. Specify read, draft, recommend, change, and submit permissions; identify approval points and accountable owners.
- Design the review and evidence record. Define review criteria and exception handling, and ensure the run record connects inputs, configuration, actions, output, decision, changes, and timing.
- Test the configured process. Check ordinary cases and relevant failure conditions, including missing or conflicting information and actions outside the intended boundary. Record results and correct control gaps before relying on the process.
- Operate with monitoring and change assessment. Assign an owner to follow failures, overrides, evidence gaps, and configuration changes; investigate and remediate issues through the organization’s control process.
This sequence translates COSO’s lifecycle-oriented control guidance and the IIA and IAASB perspectives into implementation steps. It is not a certification checklist, and completing it does not guarantee that a particular system or process is auditable. The control evidence and outcomes must be evaluated in the organization’s actual accounting context.
What the available guidance does—and does not—establish
The named guidance supports a control-first approach: apply an internal-control framework to GenAI, identify AI that can affect financial information, review outputs before accounting use, consider documentation implications when automated tools are involved, and assess AI governance and risks through an internal-audit lens. The sources do not establish the control design of any specific vendor platform, the requirements applicable in every jurisdiction, or quantitative outcomes for agent use in financial close.
No close-specific adoption, auditability, or cycle-time improvement figure is established by these sources. Claims about savings, accuracy, or close acceleration therefore need separate evidence for the organization and use case in question. COSO’s examples of potential finance uses describe application areas, not measured results for a particular deployment.
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