The Internet stands at a historic turning point. Over the past decade, we have witnessed generative artificial intelligence move from the research lab into the mainstream, permeating core business operations and daily life with astonishing speed. Yet, the AI applications most users are familiar with—whether web-based chat interfaces or enterprise automation tools—remain fundamentally confined within “walled gardens”: a single user issuing natural language prompts to a single model, with all decisions and execution tightly bounded and controlled.

However, this wave is rapidly receding. With the maturation of autonomous AI agents, the next generation of the Internet is evolving toward an “Agentic Web.” In the near future, AI will no longer serve merely as a passive tool that answers questions; it will step outside corporate perimeters to act on behalf of humans, dynamically collaborating, resource-allocating, and executing automated transactions with the AIs of other organizations across the open network.

It is precisely at this critical juncture of transformation that a long-neglected structural crisis has surfaced: when AI agents begin crossing boundaries and handing off tasks to one another across the open network, we lack a sufficiently robust, trusted, and legally binding “handoff protocol.” Fundamentally, contemporary AI interoperability faces a massive “infrastructure deficit,” rooted in the fact that we are attempting to support a machine economy requiring absolute public trust using technologies built upon “blind trust.”

The Paradigm Shift in Trust: From Human Intuition to Machine Scale

Reviewing the history of the Internet, every major architectural leap has been accompanied by the establishment of foundational trust mechanisms. From the early days of static web pages to social and e-commerce webs centered on identity and interaction, humans have consistently remained the core operators of the digital realm. When we execute credit card transactions or sign digital contracts online, we do so backed by certificates, public key infrastructure (PKI), and mature legal contracting frameworks acting as our safety net.

In the world of AI agents, however, this trust model—reliant on human intuition and legal contracts—breaks down entirely.

When an agent must hand off a task to another vendor’s automated quotation system to complete a cross-border project on your behalf, the interaction between them occurs at machine speed. Throughout this process, no human sits in front of a screen clicking “approve,” nor is there time to review physical contracts clause by clause.

If this handoff lacks transparent, verifiable, and tamper-resistant infrastructure, the entire agentic web will be built upon shifting sands. This is precisely the infrastructure deficit we must confront.

Dissecting Handoff Ambiguity: Three Critical Pain Points in AI Interaction

To understand this crisis, we must deeply examine the most vulnerable link in contemporary AI agent interaction: the consistency and integrity of handoff protocols.

In traditional software engineering, API integration is explicit and strictly defined. Systems invoke one another by passing fixed parameters and returning expected formats. Yet, handoffs between AI agents are fraught with ambiguity, manifesting in three critical pain points:

Context Distortion and Intent Skewing

Natural language is flexible, but it is also ambiguous. When a human assigns a complex task to an AI secretary—such as finding cost-effective resources while ensuring security compliance—that AI must translate the semantic intent into instructions for a cloud provider’s AI agent. Across multiple rounds of transfer and decomposition, original constraints, security boundaries, and business logics are highly susceptible to being distorted, diluted, or omitted in translation, leading the receiving AI to make high-risk decisions that deviate from the original intent.

Authorization Boundaries and Provenance Blind Spots

When one agent hands a task over to another, how does the receiving end verify whether the opposing AI genuinely holds valid authorization from the claimed enterprise? Under current technical architectures, a universally recognized “digital passport” to instantly prove an AI agent’s identity and scope of authorization is conspicuously absent. This is akin to a stranger in the physical world claiming to represent a major corporation with full authority, yet presenting no official credentials whatsoever—while you are expected to hand over your corporate master keys.

The Accountability Vacuum

If a handoff protocol malfunctions during execution, resulting in catastrophic financial loss or a data breach, how should liability be assigned legally and contractually? Is the fault attributable to the originating user failing to articulate instructions clearly, the intermediate agent distorting intent during the handoff, or the receiving agent improperly expanding its operational scope? Lacking a verifiable, auditable cryptographic trail, such disputes will devolve into endless finger-pointing, creating a severe accountability vacuum.

Why Traditional Cybersecurity Models Fall Short

Facing these challenges, one might ask: Do we not already possess mature web security certificates and authorization mechanisms? Why can they not be directly applied to AI agents?

The answer lies in the fundamental distinction between autonomy and dynamicity. Traditional security mechanisms were designed for application authorization under direct human control, where you explicitly know you are granting a specific application access to data. In contrast, autonomous AI agents are characterized by dynamic discovery and dynamic decision-making. An agent may autonomously scan the network to find the most suitable partner, negotiate contracts, and execute handoffs on the fly. Such impromptu machine-to-machine (M2M) collaboration is entirely beyond the capacity of traditional pre-bound, statically authorized architectures.

A deeper concern lies in the risk of Internet fragmentation. If major tech giants pursue isolationist strategies, establishing proprietary internal AI identity walls and handoff verification silos, future AI interoperability will regress into a digital feudalism. Enterprises and users will be forced to take sides, stifling cross-platform AI agent collaboration and thoroughly violating the open, neutral ethos of the Internet.

Finding the Solution: Bridging DNS and Cryptographic Credentials

Bridging this infrastructure deficit cannot rely on the closed, proprietary protocols of any single commercial titan; rather, we must return to the most core and neutral layers of Internet infrastructure. This represents the core significance of forward-looking identity architectures and initiatives—such as those leveraging the Domain Name System (DNS) alongside cryptographic proofs—currently being championed within the internet governance community.

Minting Digital Passports for AI Agents

Just as the early Internet utilized DNS to establish globally recognized identities for servers, future AI agents require deep binding with domain names. By anchoring an agent’s identity credentials within DNS infrastructure and robust cryptographic proofs, any entity receiving a request from an external AI agent can verify in milliseconds through a public, transparent, and decentralized mechanism:

• Exactly which legal corporate entity stands behind this AI agent?
• Does it hold valid authorization to execute this specific task?

Establishing Standardized, Auditable Machine Handoff Protocols

We must advance open, standardized M2M protocols that encapsulate intent and authorization within cryptographic signatures inside handoff payloads. When agents transfer tasks between one another, the handoff protocol functions much like a cryptographically bound digital smart contract, explicitly defining boundary limits, context integrity, and an undeniable audit trail—fundamentally neutralizing the ambiguity inherent in natural language handoffs.

Conclusion: No Public Trust, No True Agentic Economy

The next wave of artificial intelligence will not be dictated by how many parameters a model possesses, but by how robust a trust infrastructure we can construct across the open Internet.

The current infrastructure deficit in AI interoperability fundamentally reflects our attempt to power a multi-trillion-dollar autonomous agent economy using digital instruments devoid of contractual and identity credibility. Without resolving the ambiguity of handoff protocols, AI agents will remain perpetually confined within secure yet narrow corporate silos, failing to unleash their true, macro-scale potential.

For internet governance experts, policymakers, and technical architects, the time for action is now. We must collaborate to advance the roots of trust from fragile human intuition to resilient machine cryptography, establishing the indispensable institutional and technical jigsaw puzzle for the global digital economy before the agentic web fully erupts.