How to design AI-driven approvals with sufficient evidence, controls, and authority documentation to satisfy audit requirements at any point in time.
For the accountability side of agentic governance, start with Human Accountability in Agentic Workflows.
Agentic workflows introduce a new challenge: decisions may be influenced by dynamic context, multiple tools, and non-deterministic reasoning. That doesn't make auditability impossible — it just means you need to design for evidence.
Definition: Audit-ready agentic approvals are AI-driven decisions designed with sufficient evidence, controls, and authority documentation to answer who granted the agent authority, under what constraints, and what actions were executed — at any point in time.
The EU AI Act (Article 14) now requires human oversight and authority structures for high-risk AI systems — making audit-ready agentic approvals a regulatory requirement, not just a governance aspiration. According to a BrightEdge study, organizations implementing structured governance data saw a 44 percent increase in compliance visibility, and the same principle applies to agentic audit trails: structured evidence beats reconstructed narratives.
Audit-ready doesn't mean you can reconstruct every internal model step. It means you can reliably answer:
The goal is defensible, consistent governance.
A practical control stack looks like this:
| Control Layer | Purpose | Evidence Produced | Common Gap |
|---|---|---|---|
| Authority grant | Controlled delegation to the agent (scope, limits, dates, owner) | Versioned delegation record with effective dates | Grants documented informally or without expiry |
| Preconditions | Rules that must be satisfied before execution | Precondition check log (legal review, budget check) | Preconditions exist in policy but not enforced in workflow |
| Approval capture | Record of human approvals when required | Approval record with identity, timestamp, rule reference | Approval captured but rule reference missing |
| Execution evidence | System logs showing actual action taken | Transaction log from system of execution | Logs exist but not linked to authority record |
| Monitoring & exception handling | Alerting and review for out-of-band behavior | Exception reports and resolution records | Monitoring exists but exceptions not formally closed |
If any layer is missing, you'll end up relying on after-the-fact explanations.
For one action (e.g., agent initiated vendor renewal), an evidence bundle should include:
This mirrors human DOA audits (see DOA and SOX/Internal Controls Q&A for the traditional evidence model), with added emphasis on scoping and monitoring. West Monroe's 2026 research found that each request for additional analysis adds an average of three weeks of delay — proactive evidence design eliminates the reconstruction cycle that makes audits expensive.
The hardest audit question remains the same:
Did the actor have valid authority on that date?
For agents, that means:
Without that, you can't prove authority retrospectively. The EY/Society for Corporate Governance study found that roughly 90 percent of companies have DOA policies but struggle with the evidence that auditors actually need — this gap is even wider for agent-driven actions where the "as-of" question applies to a non-human actor.
Our recommendation: Implement immutable delegation records for agents from day one — even during pilot phases. The most expensive audit finding is not "the agent exceeded its authority" but "we cannot prove what authority the agent had at the time of the action." Retroactive evidence construction costs orders of magnitude more than proactive record-keeping.
If you require a human to approve every agent action, you'll lose much of the value. Instead, many organizations move to:
This creates speed without abandoning control.
Aptly helps maintain controlled authority grants (including to non-human actors) with effective dating and audit-ready history. When paired with workflow integrations, it supports consistent enforcement and evidence capture across systems.
You don't audit the reasoning — you audit the authority and evidence chain. The questions are: was the agent authorized to act, were the preconditions met, was the action within delegated limits, and was the outcome recorded? This is the same framework used for human decisions, where auditors don't reconstruct a person's thought process — they verify the authority and evidence trail.
The EU AI Act (Article 14) requires human oversight and authority structures for high-risk AI systems. SOX Sections 302 and 404 require effective internal controls over financial reporting, which extends to any agent executing financial transactions. MiFID II requires clear governance for decision-making in financial services. APRA CPS 510 in Australia requires documented delegation frameworks that would encompass AI actors operating within regulated institutions.
Follow the same retention requirements as human approval evidence — typically 7 years for financial transactions under SOX, longer for regulated industries. The delegation record, precondition logs, execution evidence, and exception records should all be retained for the same duration. Because storage costs are minimal relative to reconstruction costs, most organizations default to retaining agentic evidence for at least as long as comparable human evidence.
Yes, provided the control framework meets the same evidence standard as human approvals: documented authority grants, versioned delegation records with effective dates, captured approvals with identity and timestamps, and exception handling with formal resolution. The key is demonstrating that the agent operated within a controlled framework — not that a human reviewed every individual transaction.
Connect with our team for a discovery session to learn more about how Aptly can help within your organization. If you are already a client and need support, contact us here.
