Enterprise AI agents governance should limit authority, Flagright CTO says

Flagright Co-Founder and CTO Madhu G. Nadig said enterprise ai agents governance should focus less on the number of automated tasks and more on how safely companies delegate authority. In a PYMNTS eBook, “Building the Agent-Ready Payments Enterprise,” Nadig argued that the business impact of agentic AI will depend on clear operating limits, evidence standards and escalation rules.

Nadig’s central distinction is between AI systems that advise and AI systems that take action. Once software can pull records, change files, address service cases, contact customers or start other workflows, the main management issue becomes who has authority for each decision and how that decision can be checked.

He described the most scalable approach as “bounded autonomy,” meaning agents should receive narrow assignments under explicit rules rather than broad discretion over an entire function. Under that model, an agent follows an approved process, records what it did, shows the evidence supporting its conclusion and stops when confidence levels, policy limits or risk thresholds are crossed.

How should enterprise AI agents be governed?

According to Nadig, companies need machine-readable policies, controlled access to reliable data, testing before deployment and ongoing monitoring after agents go live. Each action should be traceable to the policy version, model version, data source and authority level that permitted it, with a working human override available when needed.

That framework is especially relevant in regulated finance, where decisions must be defensible after the fact. In financial crime compliance, Nadig said agents can review routine monitoring and screening alerts, collect information from multiple systems, compare activity with institutional policies, draft case narratives and close clear false positives.

Human investigators, in his view, should remain responsible for more complex networks, unclear conduct, regulatory judgment and decisions affecting customers. The operating change is that automated systems can take on repeated evidence-gathering work while people retain responsibility for consequential assessments.

Nadig said this shift will alter staffing needs, though he did not frame it as broad headcount elimination. Entry-level work would move away from manual triage, while teams would need more employees who can convert policy into executable workflows, test agent behavior, monitor exceptions and strengthen controls.

He also argued that domain expertise becomes more important as agents are deployed, because automated systems magnify both the quality and the flaws of their instructions. A weak policy or inconsistent process can be repeated at scale if it is embedded in an agent workflow.

The economics of automation may also change how companies handle compliance volumes. Nadig said functions that previously expanded by adding reviewers could process more activity without a matching rise in staff, but he argued that resulting savings should support stronger investigations, better control design and exception handling rather than reduce accountability.

Customer service could improve when low-risk cases are handled faster and more consistently, Nadig wrote. He cautioned, however, that in regulated or high-impact workflows, speed without a reliable audit trail can increase liability rather than improve the customer experience.

Nadig said many autonomy programs are likely to stall when companies automate tasks before repairing fragmented data, conflicting procedures, unclear decision ownership and inadequate audit records. He expects leading enterprises to measure progress by the share of decisions that can be delegated with defined controls, reliable evidence and accountable escalation, rather than by the number of agents deployed.

This story draws on original reporting from PYMNTS.