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Preprint · May 2026

Enterprise AI Agent Ecosystems: Architecture, Economics, and the Composition Gap

Shinde, A., Shinde, S. S., Bava, N., Deodhar, A., and Gupta, V. · SSRN

Status
Preprint

Shared publicly as a preprint. A preprint has not been through peer review at a journal or conference.

Venue
SSRN
Year
5 May 2026

What the paper is about

Analyses enterprise AI agent ecosystems across architecture, protocols, economics and adoption, and the gap between available agent capabilities and measurable enterprise value.

Abstract

Between late 2022 and early 2026 the dominant unit of enterprise AI deployment shifted from the standalone model to the agent: a system that observes, reasons and acts through external tools. The article analyses enterprise AI agent ecosystems as the intersection of the Internet of AI Agents and the Agentic Web, bounded by enterprise governance. It surveys the deployment baseline across finance, healthcare, operations and software engineering; decomposes the architectural, protocol and economic layers; and catalogues the adoption constraints that separate capability availability from measurable value. Four independent analytical paths — architectural, protocol, economic and adoption — converge on the same missing layer: a standardised, latency-bounded, federated Trust-Aware Ranking System (TARS) that composes cryptographic identity, behavioural reputation and task-context fitness into agent selection at the control plane. This composition gap, rather than any capability limitation, is identified as the binding constraint on the next phase of enterprise value capture.

A survey and position paper: it synthesises published evidence and proposes TARS; it does not report a new experiment.

My contribution

Co-author. Contributed to the analysis of agent discovery, selection, trust, governance and economic constraints that supports a proposed Trust-Aware Ranking System (TARS) for context-aware agent selection.

Reference

Shinde, A., Shinde, S. S., Bava, N., Deodhar, A., and Gupta, V. (2026, May). Enterprise AI Agent Ecosystems: Architecture, Economics, and the Composition Gap. SSRN.

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