Agentic AI: re-architecting assurance for a world of continuous risk

In a world of continuous risk, agentic AI can elevate trust, governance and accountability through human and agent collaboration.

In brief

• Agentic AI represents a leap forward in engagement accuracy and insight.
• Full agentic systems orchestrate both digital teammates and complete workflows while humans retain oversight and governance.
• Agentic AI delivers benefits to both clients and assurance practitioners enhancing trust, governance and accountability.

Assurance has always been about trust, but the way trust is established is changing. Today’s assurance teams are operating in an environment defined by exploding data volumes, increasingly interconnected systems and rising expectations for insight, speed and consistency. While AI tools have helped automate individual tasks, much of the assurance process still relies on humans manually coordinating data, analyses and decisions across fragmented workflows.

Rather than acting as isolated tools, agentic systems bring together multiple AI agents that can take initiative, collaborate across tasks and adapt to changing conditions, operating within human-defined boundaries, with assurance professionals remaining firmly responsible for judgment, professional skepticism and final conclusions. The foundations of this technology stack are already in place. As agentic capabilities continue to mature, we will progress from human-led work supported by an assistant, to humans collaborating with coordinated agent teams and ultimately to true human-led agentic workflows—where humans set intent and oversight while agents execute end-to-end tasks.

Grounding the conversation — what is an AI agent workflow?

What distinguishes agentic AI from general AI is a set of characteristics that allow agent capabilities to operate in an autonomous workflow with human-in-the-loop oversight where needed. Think of an agent as a digital teammate that can help assurance professionals, among others, by:

These are just a few examples of how AI agents act as digital teammates that enhance the efficiency and effectiveness of auditors, helping them manage data complexity and improve the quality of their work without replacing the critical human element of judgment and decision-making. A fully automated agentic system, however, can provide another dimension of a decision support mechanism where auditors can focus on higher order strategic work.

Agents vs. agentic systems

Agents can be thought of as single-task digital teammates. Each agent operates with a clear purpose, utilizing various capabilities to execute its assigned duties. For instance, one agent might be responsible for pulling financial data from different sources and formatting it for analysis, while another could be tasked with reviewing documents for compliance.

On the other hand, full agentic systems represent a more comprehensive approach that integrates multiple agents with an orchestrator to coordinate the interactions between agents and between the system and user. Agentic workflows and orchestrators can manage an increasing number of elements of assurance engagements across the end-to-end process. Initially, agents start as individual solutions handling discrete tasks, then evolve into linked workflows. Various agents collaborate, with the potential that each handles different aspects of the audit. For example, while one agent focuses on data preparation, reconciling five different files to cleanse or eliminate duplicate data, another might assess the risks within the data, flagging anomalies or unusual patterns.

Orchestrators play a vital role in coordinating these agents, ensuring that the right assignments go to the right agents, that their outputs feed seamlessly into the overall audit workflow and that professionals are engaged to approve taking important actions or review key outputs. For example, an orchestrated agentic system could ingest client transaction data daily, reconcile it across sub-ledgers, flag emerging anomalies, request supporting evidence from client systems and escalate only unresolved items to the audit team — while maintaining a complete, auditable trail of every action taken. Ultimately, these linked workflows have the potential to become fully automated, supporting end-to-end processes where appropriate.

Humans teaming with agents

A key aspect of this transformation is the concept of human and agent teaming. In this collaborative environment, auditors remain firmly in charge, exercising their professional judgment and skepticism, as well as approval authority.

This partnership with agents does not make the assurance professional’s role obsolete; rather, it resembles having a reliable digital teammate who is always available and never panics. Agents take on the burden of routine tasks, allowing auditors to engage more deeply with their work and make informed decisions based on the insights provided.

Benefits for practitioners and clients

The integration of agentic AI into the auditing process brings significant benefits for both practitioners and clients, enhancing the overall effectiveness and efficiency of assurance services.

Benefits for assurance professionals

With the assistance of AI agents, practitioners can expect to encounter fewer manual steps in their daily workflow, benefiting from the automation of both frequent tasks as well as less frequent tasks that would otherwise require relearning how to perform them. Moreover, the standardization of quality across engagements becomes a hallmark of this new approach. By utilizing consistent processes and documentation practices, auditors can ensure that their outputs meet high standards, regardless of the specific engagement. This consistency fosters confidence in the results produced and enhances the credibility of the audit.

Another significant advantage is the ability to gain more actionable insights into what truly matters earlier in the process. Unlike traditional analytics — which often analyze 100% of transactions at defined points in the audit — agents can continuously analyze data from multiple data sources as it becomes available. By autonomously running pattern checks, applying context from prior-period or industry data and suggesting actions to take next, agents can surface potential risks or considerations.

Additionally, the use of a unified interface simplifies interactions with various tools and systems. Instead of navigating through multiple platforms, practitioners can engage with a single interface, enhancing their efficiency and reducing frustration.

Benefits for clients

Clients stand to gain considerably from the implementation of agentic AI in the execution of assurance services. One of the most notable benefits is the ability to consistently execute across engagements, regardless of size or complexity. Experienced professionals are supported by technology that reinforces the consistent application of procedures, applies standardized validation checks and identifies common risks within a particular industry, process or function.

The integration of AI agents also streamlines communication between assurance professionals and clients. For example, automating routine requests for additional clarification or supporting information for anomalies identified by the agent. Over time, this model also opens the door to agent-to-agent interactions, where client and assurance systems can exchange requests, updates and supporting evidence automatically, reducing manual coordination while preserving human oversight. This streamlined communication fosters a more seamless collaboration.

Clients also benefit from faster identification of issues. With agents continuously monitoring and analyzing data, potential problems can be flagged early in the process, allowing for quicker resolutions. This agility not only enhances the audit experience but also helps clients maintain their operational momentum.

As the volume and complexity of data continue to grow, clients can rely on an AI-augmented team that is equipped to keep pace. The combination of human judgment and AI capabilities helps ensure that assurance professionals can effectively manage and analyze large numbers of data sets, providing clients with unified insights that are both timely and relevant.

Taking action — building a full orchestration layer

As we navigate the new landscape of auditing and assurance, it’s essential to reframe our understanding of artificial intelligence. Rather than viewing AI solely as a productivity enhancer, we can recognize it as a fully orchestrated system. A framework that not only supports assurance professionals in their tasks but also establishes a new standard for the client experience. By embracing properly governed agentic systems with appropriate controls, we can exceed human consistency in repeatable execution and set a benchmark for auditable AI that enhances the integrity of financial systems.

In this new collaborative environment, assurance professionals will leverage the strengths of AI agents while maintaining their critical oversight and decision-making capabilities. This partnership will underscore the importance of the auditor’s role, providing judgment, experience and accountability.

The integration of agentic AI into assurance is not just a technological advancement; it is a paradigm shift that will redefine how we approach trust, governance and accountability in our work. Each new model and agent-driven approach will bring with it new capabilities over the coming months and years. By embracing this change, we can elevate the client experience and set a new standard for excellence in the profession.