Agentic AI systems are fundamentally reshaping the business landscape we know today. They are not just advanced algorithms, but autonomous agents that can perceive, reason, and act to solve complex challenges without human supervision. While potential efficiency gains are enormous — as Gartner affirms, "Agentic AI will transform businesses" — this autonomy introduces a critical new challenge: The need for control over AI.

The risk of governance gaps

The transition from a "human-in-control" model to a "human-in-the-loop" model demands a revolutionary approach to governance. Proactive oversight replaces reactive correction. Without a robust governance framework, the very features that make agentic AI powerful can make the systems they operate in fragile:

• Opaqueness: Autonomous decision-making can become an impenetrable "black box".

• Auditing challenges: Tracing autonomous actions strains compliance and forensic accountability.

• Accountability failure: When an agent fails, responsibility becomes murky without transparent control logic.

The regulatory imperative

Global regulators are already responding to the AI transformation. The EU AI Act, for instance, will soon classify many agentic AI applications — including financial credit scoring, risk modeling, and underwriting — as high-risk cases. A structured, traceable decision-support framework is the essential bridge between radical autonomy and regulatory accountability. This mandates non-negotiable compliance requirements:

• Robustness and accuracy: Systems must perform reliably and predictably.

• Transparency and documentation: Clear visibility into the system's logic and reasoning is mandatory.

• Human oversight mechanisms: Protocols for human intervention and control must be embedded.

Governance by design

AI governance must span the entire lifecycle of enterprise systems, from initial design to continuous improvement. Compliance is a continuous process, not a one-time check:

Lifecycle implementation guidance

To meet the requirement for explainability and observability, enterprises must embed transparency using techniques like execution graphs, confidence scores, and traceable reasoning chains. This ensures both technical and non-technical stakeholders can understand and assess AI-driven outputs, fostering accountability and trust.

Cnext's approach towards trustworthy agentic AI

At Cnext, in partnership with PKF Bofidi, we're tackling the autonomy-control dilemma by building governance directly into the architecture of our agentic AI platform, Kairos.

Kairos embeds decision support and auditable guardrails at the foundational level. Every autonomous action is governed by principles of fairness, accountability, and compliance and is engineered into how the agent thinks and acts. By integrating advanced AI operations with PKF Bofidi’s expert audit and governance methodologies, we deliver agentic AI that your organization can truly trust. With verifiable control and regulatory alignment built-in, our clients can safely benefit from the potential of agentic AI.