HomeResearch LibraryBeyond Task Completion: An Assessment Framework for Eva…
Chapter 3 · 2025

Beyond Task Completion: An Assessment Framework for Evaluating Agentic AI Systems

Sreemaee Akshathala, Bassam Adnan, Mahisha Ramesh

Abstract

This paper proposes an end-to-end Agent Assessment Framework for evaluating agentic AI systems beyond traditional task completion metrics. It addresses the challenges posed by the non-deterministic nature of LLM agents and multi-agent architectures, focusing on four evaluation pillars: LLMs, Memory, Tools, and Environment. The framework aims to capture runtime uncertainties and behavioral deviations overlooked by conventional methods.

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 Agentic AI, assessment framework, evaluation 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

Agentic AIassessment frameworkevaluationLLM agentsmulti-agent systems