How agentic AI enhances audit quality within a human-directed framework

In our previous article, we defined agentic AI as a workflow-aware, governance-bound evolution of artificial intelligence in audit and accounting. Unlike prompt-driven tools, agentic systems are designed to operate continuously in support of structured objectives.

The natural next question is: how does this actually work inside an engagement?

How agentic AI functions in practice

One helpful way to understand agentic AI is through a recurring operational cycle:

Prepare → Analyze → Review

This is not a rigid formula, but it reflects how agentic systems support professional workflows.

First, the system prepares. It observes engagement data, workflow progress and predefined triggers. Rather than waiting for a prompt, it continuously monitors relevant inputs within the engagement workflow and established parameters.

Next, it analyzes. Based on contextual rules and engagement objectives, it determines appropriate next steps. That may involve identifying inconsistencies, highlighting incomplete procedures or surfacing potential risk indicators based on engagement context and predefined criteria.

Finally, it supports review. It can prepare documentation drafts, flag potential gaps or escalate issues according to defined thresholds, always within governance controls.

The system provides guided support and recommendations within clearly defined boundaries.

Human-directed by design

In professional services, independence and accountability are non-negotiable. Agentic AI is therefore designed to operate under human direction.

Professional judgment, conclusions and sign-offs remain the responsibility of qualified practitioners. The system supports that responsibility by reducing administrative burden and improving visibility across the engagement.

This human-directed structure typically includes:

• Role-based access permissions

• Defined approval checkpoints

• Escalation pathways

• Clear audit trails and data lineage

These systems operate within firm-defined methodologies, reinforcing established approaches rather than replacing them.

Rather than replacing professionals, agentic AI reinforces the profession’s emphasis on skepticism and defensibility.

Governance is foundational

One of the most important aspects of agentic AI in audit environments is governance.

Strong implementations embed controls such as:

• Traceable data flows

• Documented rule logic

• Clearly defined action thresholds

• Oversight mechanisms for escalations

Without governance, advanced automation introduces risk. With governance, it enhances confidence.

For firms operating in regulated environments, this distinction is critical.

Practical impact on audit quality

When thoughtfully implemented, agentic AI can contribute meaningfully to audit quality.

By operating continuously within defined boundaries, it can:

• Surface issues earlier in the engagement lifecycle

• Reduce rework by identifying gaps before review stages

• Improve consistency across teams by supporting standardized workflows and firm-defined methodologies

• Strengthen documentation defensibility

Importantly, these benefits are not automatic. They depend on strong data foundations, disciplined system design and active professional engagement.

Technology supports quality. Governance sustains it.

The path forward

Agentic AI is not a departure from professional principles. It is an evolution aligned with them.

Firms are likely to continue adopting human-directed AI models that emphasize accountability and control. Those that invest now in governance frameworks and workflow clarity will be better positioned to harness emerging AI capabilities without compromising regulatory confidence.

This visibility also supports firm-level oversight, consistency and inspection readiness.

Agentic AI does not change who is responsible for audit quality. It changes how consistently that responsibility can be supported.

Want to learn more? Download our eBook, Agentic AI: What the Audit and Accounting Profession Needs to Know, to explore governance considerations, practical applications and the path forward for firms adopting AI responsibly.  

‍