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arxiv: 2605.23459 · v1 · pith:QHYFYSROnew · submitted 2026-05-22 · 💻 cs.SE · cs.AI

AI Assurance: A Comprehensive Testing Strategy for Enterprise AI Systems

Chitra Badagi , Divye Singh , Animesh Sen , Adinath Shirsath This is my paper

Pith reviewed 2026-05-25 03:28 UTC · model grok-4.3

classification 💻 cs.SE cs.AI
keywords AI assuranceenterprise AI systemstesting strategyrisk reductionfailure taxonomyassurance pyramidRAG testingLLM evaluation
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The pith

Enterprise AI systems require continuous risk reduction through evaluation rather than classical correctness verification.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper argues that enterprise AI systems built on large language models, retrieval pipelines and autonomous agents are probabilistic, context-sensitive and emergent, so they cannot be verified as correct in the classical sense but only evaluated with increasing confidence. It proposes three principles: testing must target ongoing risk reduction, evaluation must stand as a core engineering discipline alongside development, and AI failures produce organizational impacts unlike those in deterministic software. To operationalize this, the authors introduce a structured AI Failure Taxonomy and a revised five-layer AI Assurance Pyramid, along with guidance on evaluation-driven development, RAG testing, model lifecycle management and governance.

Core claim

The paper claims that because enterprise AI systems are probabilistic and emergent they cannot be verified to be correct but only evaluated for rising confidence, so assurance must center on continuous risk reduction, treat evaluation as an engineering discipline, and account for distinct organizational failure modes via an AI Failure Taxonomy and a five-layer AI Assurance Pyramid that supplies practical guidance for RAG systems, lifecycle management and governance.

What carries the argument

The five-layer AI Assurance Pyramid, which structures assurance activities to manage the probabilistic and emergent risks of enterprise AI systems.

If this is right

  • Evaluation activities run in parallel with development as a standard engineering practice.
  • Testing protocols must specifically address retrieval-augmented generation pipelines and autonomous agents.
  • Model lifecycle management incorporates continuous assurance checkpoints rather than one-time verification.
  • Governance structures adapt to emergent behaviors and their distinct organizational consequences.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The approach could be piloted inside existing DevOps pipelines to measure integration effort.
  • Quantitative risk metrics derived from the taxonomy might be compared against current incident logs.
  • The pyramid structure could inform new standards for AI system audits in regulated industries.
  • Failures that fall outside the taxonomy might reveal additional categories needing inclusion.

Load-bearing premise

The introduced AI Failure Taxonomy and five-layer AI Assurance Pyramid constitute a comprehensive and operationally deployable strategy that addresses the unique risks of probabilistic enterprise AI systems.

What would settle it

An enterprise AI deployment in which applying the taxonomy and five-layer pyramid produces no measurable reduction in risk or where traditional deterministic testing methods prove equally effective at preventing organizational impacts.

Figures

Figures reproduced from arXiv: 2605.23459 by Adinath Shirsath, Animesh Sen, Chitra Badagi, Divye Singh.

Figure 1
Figure 1. Figure 1: The AI system complexity spectrum. Each level adds new properties and demands distinct [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: RAG system architecture showing two independent failure surfaces. A correct final response [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Traditional testing relies on a single expected output and a binary verdict. AI systems [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Evaluation infrastructure as the AI equivalent of CI/CD. Every change, to code, model [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The Evaluation-Driven Development loop. Behaviour is specified through the evaluation [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The AI Assurance Pyramid. Layer 0 is deterministic and cheap; layers above are probabilis [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The three change scenarios and their required evaluation discipline. Scenario 3 simultaneous [PITH_FULL_IMAGE:figures/full_fig_p024_7.png] view at source ↗
read the original abstract

Enterprise AI systems, built on large language models, retrieval pipelines and autonomous agents, introduce a class of risks that traditional software quality assurance was never designed to address. These systems are probabilistic, context-sensitive and emergent: they cannot be verified to be correct in the classical sense, but only evaluated with increasing confidence. This paper presents a comprehensive assurance strategy for enterprise AI systems built around three key principles: first, that AI testing should focus on continuous risk reduction rather than strict correctness verification; second, that evaluation must be treated as a core engineering discipline alongside development; and third, that failures in AI assurance can lead to organizational impacts that are fundamentally different from those seen in traditional deterministic software systems. We introduce a structured AI Failure Taxonomy, propose a revised five-layer AI Assurance Pyramid and provide operational guidance on evaluation-driven development, RAG system testing, model lifecycle management and governance. The goal is to equip engineering leaders and practitioners with a strategy that is both philosophically grounded and operationally deployable.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The paper presents a comprehensive assurance strategy for enterprise AI systems, which are probabilistic, context-sensitive, and emergent. It argues for three principles: focusing on continuous risk reduction instead of strict correctness verification, treating evaluation as a core engineering discipline, and recognizing that AI assurance failures have unique organizational impacts. The manuscript introduces an AI Failure Taxonomy, a five-layer AI Assurance Pyramid, and offers operational guidance on evaluation-driven development, RAG system testing, model lifecycle management, and governance, with the aim of providing a philosophically grounded and operationally deployable approach.

Significance. Should the proposed framework prove effective upon validation, it would offer engineering leaders a structured way to address the distinct risks of LLM-based enterprise systems, potentially leading to better risk management and governance practices. The introduction of a failure taxonomy and assurance pyramid provides a conceptual foundation that could influence how organizations approach AI quality assurance, distinguishing it from traditional software testing.

major comments (1)
  1. [Abstract] Abstract: The claim that the AI Failure Taxonomy and five-layer AI Assurance Pyramid constitute a 'comprehensive' and 'operationally deployable' strategy is load-bearing for the paper's central contribution, yet the manuscript supplies no case studies, before/after metrics, or controlled applications to an enterprise system demonstrating measurable continuous risk reduction or distinct organizational impact handling.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the single major comment below and agree that the abstract language requires adjustment to better match the manuscript's conceptual scope.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The claim that the AI Failure Taxonomy and five-layer AI Assurance Pyramid constitute a 'comprehensive' and 'operationally deployable' strategy is load-bearing for the paper's central contribution, yet the manuscript supplies no case studies, before/after metrics, or controlled applications to an enterprise system demonstrating measurable continuous risk reduction or distinct organizational impact handling.

    Authors: We agree that the manuscript contains no empirical validation in the form of case studies, metrics, or controlled deployments. The paper's contribution is the introduction of the AI Failure Taxonomy, the five-layer pyramid, and prescriptive guidance grounded in the three stated principles; it does not claim or demonstrate measured outcomes. We will revise the abstract to remove or qualify the terms 'comprehensive' and 'operationally deployable,' replacing them with language that describes the work as a structured conceptual framework and set of operational recommendations. This change aligns the central claims with the actual content of the manuscript. revision: yes

Circularity Check

0 steps flagged

No circularity: purely conceptual proposal with no derivations or self-referential reductions

full rationale

The manuscript introduces an AI Failure Taxonomy and five-layer AI Assurance Pyramid as prescriptive constructs for enterprise AI assurance. No equations, fitted parameters, predictions, or derivation chains exist. The three principles are stated as foundational assumptions rather than derived results. No self-citations are invoked to justify uniqueness or load-bearing premises; the work is self-contained as a strategy proposal. This matches the default non-finding for descriptive papers lacking mathematical or statistical reductions.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

No mathematical content or derivations; the proposal rests on domain assumptions about the probabilistic and emergent nature of LLM-based systems.

axioms (1)
  • domain assumption Enterprise AI systems are probabilistic, context-sensitive and emergent and cannot be verified to be correct in the classical sense.
    Stated in the abstract as the foundational premise distinguishing AI from traditional software.

pith-pipeline@v0.9.0 · 5706 in / 1092 out tokens · 31346 ms · 2026-05-25T03:28:38.835554+00:00 · methodology

discussion (0)

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Reference graph

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