Why trust is the new currency in the agentic era — and what it’s worth

The World Economic Forum calls trust “the new currency” in the agentic AI era and that’s not just a metaphor: An increase of 10 percentage points in trust directly translates to 0.5% GDP growth. But here’s what makes trust as a currency fundamentally different from any that’s come before: you can’t borrow it, you can’t buy it and you can’t simply mint more.

When it comes to AI, trust used to mean one thing — accuracy. Does the model predict correctly? Then we started asking harder questions about bias, transparency and whether we could explain the AI’s reasoning. Agentic AI changes the equation entirely. When a system doesn’t just analyze or recommend, but actually takes action, trust shifts from “Do I believe this answer?” to “Am I still in full control of what this system does?”

In the agentic era, trust must evolve from ensuring accurate results to building systems that can ensure continuous control and reliability of AI agents. As a result, trust is now the foundational architecture that separates organizations capable of deploying autonomous agents from those perpetually managing the consequences of systems they cannot safely control. My question for enterprise leaders is: Are you building that infrastructure now or will you spend next several years explaining why you didn’t?

The growing trust deficit

The numbers tell a story of eroding confidence at precisely the moment when trust matters most. According to Stanford University’s Institute for Human-Centered Artificial Intelligence, globally, as AI-related incidents surged 56.4%, confidence that AI companies protect personal data fell from 50% in 2023 to 47% in 2024.

This isn’t just a perception problem. One out of six enterprise security breaches now involves AI, yet 97% of affected companies lacked proper access controls. By 2028, Gartner estimates a quarter of enterprise breaches will trace to AI agent abuse.

Here’s the paradox: while 79% of companies have already adopted AI agents and another 15% are exploring possibilities, according to PwC, most companies have no AI-specific controls in place. In short, as companies rush to adopt agentic AI, we’re witnessing a fundamental readiness gap between vulnerabilities and defenses. Trust is eroding faster than companies can catch up.

The economics of trust infrastructure

Ironically, AI will also be your best defense, whether it’s against AI-amplified attacks by external parties or against AI agents behaving maliciously. An IBM report found that “organizations using AI and automation extensively throughout their security operations saved an average $1.9 million in breach costs and reduced the breach lifecycle by an average of 80 days.” Leveraging AI to enhance security delivers both monetary and efficiency ROI, with breaches solved an average of 80 days faster than non-automated operations. That’s not hypothetical risk management but measurable competitive advantage, especially because it enables use cases that competitors can’t risk deploying.

Traditional security was built on static trust: verify identity at the gate, then assume good behavior inside the walls. Agentic AI demands we go further. Unlike traditional applications, AI agents adapt autonomously, modify their own behavior and operate at machine speed across enterprise systems; this means yesterday’s trusted agent could potentially be today’s compromised threat that immediately reverts to normal behavior to evade detection.

Trust cannot be established and maintained just at the perimeter; our focus must shift to inside the walls as well. Securing these dynamic actors requires treating them less like software and more like a workforce, with continuous identity verification, behavioral monitoring and adaptive governance frameworks.

Successful trust architecture rests on three foundational pillars, each addressing distinct operational requirements while integrating into a cohesive security posture.

Pillar 1: Verifiable identity

Every AI agent requires cryptographic identity verification comparable to employee credentials. Industry leaders recognize this imperative: Microsoft developed Entra Agent ID for agent authentication, while Okta’s acquisition of Axiom and Palo Alto Networks’ $25 billion CyberArk purchase signal market recognition that agent identity management is critical.

Organizations must register agents in configuration management databases with the same rigor applied to employee vetting and physical infrastructure, establishing clear accountability for every autonomous actor operating within enterprise boundaries.

Pillar 2: Comprehensive visibility and continuous monitoring

Traditional security tools monitor network perimeters and user behavior but lack mechanisms to detect anomalous agent activity. Effective trust infrastructure requires purpose-built observability platforms capable of tracking API call patterns, execution frequencies and behavioral deviations in real time.

Gartner predicts guardian agents, which are AI systems specifically designed to monitor other AI systems, will capture 10% to 15% of the agentic AI market by 2030, underscoring the necessity of layered oversight mechanisms.

Pillar 3: Governance as executable architecture

Effective governance transforms policies from static documents into executable specifications that define autonomy boundaries, such as which actions agents can execute independently, which operations require human approval and which capabilities remain permanently restricted. Organizations with mature responsible AI frameworks achieve 42% efficiency gains, according to McKinsey, demonstrating that governance enables innovation rather than constraining it — provided the governance operates as an architectural principle rather than a compliance afterthought.

Research from ServiceNow and Oxford Economics’ AI Maturity Index reveals that pacesetter organizations that are achieving measurable AI benefits have established cross-functional governance councils with genuine executive authority, not technical committees relegated to advisory roles.

In sum, trust infrastructure isn’t defensive. It’s the prerequisite for deploying AI agents in high-value workflows where competitive advantage actually resides, separating organizations capable of strategic deployment from those perpetually constrained by risks they cannot adequately manage.

The 2027 divide

Gartner predicts 40% of agentic AI projects will be canceled by 2027, citing inadequate risk controls as a main factor. By then, there will be a clear divide between organizations that can safely deploy ambitious agentic use cases and those that cannot afford to. The former will have built trust as infrastructure; the latter will be retrofitting security onto systems already deployed and discovering problems through costly incidents.

Trust can’t be borrowed from consultants or bought from vendors. Unlike traditional currencies that flow freely, trust in the age of agentic AI must be earned through verifiable governance, transparent operations and systems designed with security as a core principle, not an afterthought. As the gap between those who have it and those who don’t widens, the architectural decisions you make today will determine which side of the divide you’re on.

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