# Infrastructure for the Agentic Web: Gap Analysis and Architecture from the Agentverse Platform

arXiv: https://arxiv.org/abs/2606.20570 · HTML: https://mlsanity.com/paper/2606.20570/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 26 Apr 2026.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 5 of 5)

- [Zep: A Temporal Knowledge Graph Architecture for Agent Memory](https://mlsanity.com/paper/2501.13956/) (2025)
- [Infrastructure for AI Agents](https://mlsanity.com/paper/2501.10114/) (2025)
- [An Economy of AI Agents](https://mlsanity.com/paper/2509.01063/) (2025)
- [AI Agents with Decentralized Identifiers and Verifiable Credentials](https://mlsanity.com/paper/2511.02841/) (2025)
- [When Intelligence Overloads Infrastructure: A Forecast Model for AI-Driven Bottlenecks](https://mlsanity.com/paper/2511.07265/) (2025)

## Built on by (most cited first, 1 of 1)

- [Protocol-Level Attacks on Agentic Commerce Platforms: A Cross-Platform Taxonomy, AIP-Bench, and Unified Defense](https://mlsanity.com/paper/2607.21824/) (2026)

## Abstract (arXiv)

The emergence of autonomous AI agents as first-class participants in digital infrastructure marks a fundamental inflection point in the evolution of the Web. While significant research has been directed at agent behaviour and reasoning, comparatively little attention has been paid to the infrastructure those agents require to operate reliably at scale. This paper addresses that gap with a systematic analysis of Agentverse, the agent cloud platform developed by Fetch.ai under the Artificial Superintelligence (ASI) Alliance, which represents one of the most mature production deployments of agent-native infrastructure available today. We make three principal contributions. First, we conduct an empirical audit of the Agentverse platform, cataloguing 204 API endpoints (Q1 2026) and characterising what is operational, partially deployed, or absent. From this audit we derive a Gap Taxonomy of eight categories encompassing 62 distinct missing capabilities, ranging from agent memory and observability to security, economic primitives, and enterprise scaling. Second, we propose a seven-layer Agent Cloud Stack -- a reference architecture for what a fully realised agent-native cloud should provide by 2030, grounded in the specific gaps we identify. Third, we characterise five critical evolution paths: from ephemeral storage to a full Agent Memory Cloud; from keyword discovery to a semantic, trust-weighted Agent DNS; from a single-protocol model to a multi-standard agent lingua franca; from single-instance hosting to Kubernetes-scale orchestration; and from simple token payments to rich agent economic primitives. Together these contributions provide a diagnostic of current agent infrastructure and a technically grounded vision for what the agent cloud must become to support the agentic web -- Web4 -- by 2030.