How Agentic AI can address compliance, audit trails, and regulatory frameworks

As enterprises scale their adoption of AI, compliance, transparency, and regulatory adherence have emerged as non-negotiable priorities.

Agentic AI—a paradigm where autonomous AI agents work collaboratively under orchestration—offers a robust framework to meet these growing demands with agility, speed, and accountability.

The main areas where Agentic AI can make a difference are in enforcing real-time policy aware scalable, and traceable compliance. This can throw out insights regarding violations and outliers, which can be quickly used to introduce corrective action.

Real-Time Compliance Monitoring

Real-time compliance with observability, governance and risk monitoring is the norm today. Gartner forecasts a 50 % jump in governance, risk and compliance tool investment by 2026 as boards demand always-on monitoring rather than periodic audits. The orchestration of these controls can be managed by Agents, which can surface exceptions via alerts and notifications.

For instance, JPMorgan’s use of autonomous anti-money laundering (AML) agents triages millions of transactions every day, cutting false positive alerts by 95 % allowing investigators to intervene faster and more effectively.

Scalable, Traceable Audit Capabilities

Agentic AI enables auditable workflows at scale. Deloitte’s Omnia platform exemplifies this with “work paper agents” deployed across 85,000 auditors in over 150 countries. These agents generate traceable evidence in real-time, aligned with international standards like PCAOB and ISA. Such automation not only accelerates audits but also ensures regulatory-grade documentation and consistency across global operations.

Policy-Aware Guardrails

A critical strength of Agentic AI lies in its ability to embed guardrails directly into the system architecture. Google’s secure agent framework mandates features like named human controllers, scope restrictions, and observable logs—creating a model that organisations can replicate to enforce accountability. In parallel, the EU AI Act now requires high-risk AI systems (e.g., in healthcare, hiring, or infrastructure) to maintain granular event logs and provenance data, which policy-aware agents can generate by design.

Bridging the Trust Gap

Despite the potential, trust in GenAI adoption remains low. McKinsey’s 2024 survey found that 91% of leaders felt unprepared to scale GenAI safely, with explainability cited as a top concern. Agentic AI addresses this through built-in traceability and replay features, enabling both compliance and confidence. However, PwC reports that only 11% of organisations have fully implemented Responsible AI, highlighting the need for more transparent oversight frameworks.

A Strategic Imperative

Agentic AI transforms trust into a measurable asset—through continuous audit trails, human-in-loop validations, and explainable logic. For boards and regulators alike, this means defensible controls, not just claims. As compliance moves from a checkbox to a strategic lever, Agentic AI is fast becoming the backbone of future-ready, trustworthy AI systems.

Kanakalata Narayanan

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Kanakalata Narayanan is Vice President, Engineering at Ascendion.