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Chapter 7 · 2026

AI Agent Systems: Architectures, Applications, and Evaluation

Bin Xu, Yao-Hung Hubert Tsai, Ruslan Salakhutdinov

Abstract

This survey synthesizes the emerging landscape of AI agent architectures, covering deliberation, reasoning, planning, control, tool calling, and environment interaction. It organizes prior work into a unified taxonomy spanning agent components, orchestration patterns, and deployment settings. The paper discusses key design trade-offs and highlights challenges in evaluation due to non-determinism, long-horizon credit assignment, and tool/environment variability.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper
Eigenvector Research — Marco van Hurne
How this paper contributes to solving the Zone III problem (PASF-PADE)

This paper directly addresses one of the core structural challenges in Zone III deployments. The research on AI agents, architectures, applications provides evidence-based foundations that enterprise architects cannot ignore when designing long-horizon autonomous workflows. The findings challenge the assumption that a base language model — however capable — can handle the complexity of durable, governed, multi-step execution without explicit architectural intervention. For Zone III practitioners, this paper belongs in the required reading list.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Topics

AI agentsarchitecturesapplicationsevaluationtaxonomy