What is agentic AI? A practical guide for audit and accounting firms

Audit and accounting firms are navigating a period of sustained pressure. Engagements are becoming more complex. Regulatory expectations continue to evolve. Clients expect faster, more insightful reporting, supported by defensible documentation.

At the same time, firms are working within talent constraints and tight timelines. It’s no surprise that artificial intelligence (AI) has become central to strategic conversations across the profession.

But as AI adoption grows, so does confusion. Generative AI is widely discussed. Automation has been around for years. And now a new term is emerging: agentic AI.

So what is agentic AI and why does it matter for audit and accounting firms?

The AI most firms are using today

To understand agentic AI, it helps to look at how AI is typically used today.

Many firms rely on rules-based automation to execute predefined, repeatable steps. These systems are efficient and reliable, as long as the process doesn’t change. They follow instructions, but they don’t adapt.

More advanced environments incorporate data analytics and machine learning to analyze large datasets and identify anomalies or risk indicators. These tools surface valuable insights, but they still require someone to interpret the output and decide what to do next.

Then there’s generative AI, the newest and fastest-growing category. Generative tools draft summaries, prepare explanations and assist with documentation. They are powerful accelerators. But they are also reactive: they wait for a prompt, respond and stop.

Each of these technologies adds value. Yet they often operate in isolation.

That isolation can create friction inside an engagement. Manual handoffs persist. Risk signals may surface late. Documentation steps can become fragmented across tools.

In other words, AI may assist individual tasks, but it doesn’t always coordinate the full workflow.

Moving from reaction to coordination

This is where agentic AI enters the conversation.

Agentic AI is not simply “smarter” AI. Its defining characteristic is not intelligence alone, but structured intent combined with workflow-aware execution.

Rather than waiting for isolated prompts, agentic systems are designed to assist engagement workflows at defined points, within clearly established boundaries. They monitor engagement status against configured criteria, detect predefined signals, and surface or recommend permitted workflow steps in support of established objectives, all under explicit governance and human oversight.

In practical terms, agentic AI in audit and accounting:

• Operates within structured, engagement‑defined workflows

• Supports predefined objectives set by the firm

• Functions under explicit human oversight and approval controls

• Helps coordinate multiple procedural steps, rather than a single interaction

The shift may sound subtle but in practice, it’s significant.

Generative AI vs. agentic AI: Why the distinction matters

Generative AI excels at producing content — summaries, explanations and draft documentation. It improves speed and productivity.

Agentic AI builds on those capabilities by extending support to multiple stages of an engagement lifecycle. Instead of responding to a single instruction, it can help support the sequencing of

workflow steps within predefined governance, permissions and review checkpoints.

The distinction can be summarized simply:

• Generative AI supports individual tasks and responses.

• Agentic AI supports coordination across tasks within an engagement workflow.

For audit firms, coordination is critical. Engagements are multi-stage processes involving risk assessment, documentation, review cycles and approvals. Quality depends on consistency across those stages, not just speed within one of them.

Importantly, this coordination is engagement‑scoped and event‑aware, not autonomous or unsupervised, ensuring firms retain control over when and how AI‑assisted steps are initiated.

Why this evolution matters now

The audit profession is built on accountability, defensibility and professional judgment. Any technology introduced into that environment must reinforce those principles.

Agentic AI reflects an evolution toward workflow‑aware, governed assistance, where activities are logged, permissions are enforced and review points remain clearly attributable to practitioners.

For firms facing increased complexity and heightened expectations, that evolution may prove essential.

In our next article, we’ll explore how agentic AI works in practice and how firms can apply it responsibly within a governance-first framework.

‍

‍