AIOBES Foundational Paper
Authors/Creators
Description
AI governance frameworks require behavioural evidence, yet current industry testing practices cannot produce it. Architectural documentation, benchmark metrics and static prompt tests fail to reveal how AI systems behave under real operational conditions, leaving organisations unable to demonstrate stability, alignment or safety in regulated workflows.
Because AI systems do not expose their internal reasoning or decision pathways, operational behaviour is the only observable and auditable evidentiary surface available for compliance and risk management. Behavioural evidence is therefore the foundation of any governance model concerned with real‑world performance.
This paper establishes that foundation through behavioural methodologies - beginning with LLM Inquisitor and Vectored Conversational AI Testing and extending to future behavioural disciplines as operational requirements evolve. AIOBES formalises these methodologies into a structured behavioural standard for governance, compliance and safe operational deployment. It is designed to integrate directly into existing organisational governance frameworks and can be adopted by current standards bodies with minimal modification to their established processes.
Other (English)
AI governance and assurance frameworks increasingly require behavioural evidence to demonstrate stability, alignment and safety in operational workflows. However, current industry testing practices -architectural documentation, benchmark metrics and static prompt‑list evaluations- cannot reveal how AI systems behave under real operating conditions. As AI systems do not expose their internal reasoning or decision pathways, operational behaviour remains the only observable and auditable evidentiary surface available for compliance, risk management and regulated deployment.
This paper establishes a behavioural foundation for AI governance through methodologies including LLM Inquisitor and Vectored Conversational AI Testing, and outlines how future behavioural disciplines will evolve as operational requirements expand. AIOBES formalises these methodologies into a structured behavioural standard for governance, compliance and safe operational deployment. It is designed to integrate directly into existing organisational governance frameworks and can be adopted by current standards bodies with minimal changes to their established processes.
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AIOBES Foundational Paper.pdf
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Additional titles
- Subtitle
- Establishing the Behavioural Evidence Layer Ecosystem for AI Governance and Seamless Integration into Existing Standards Frameworks.
References
- Evaluating & Testing AI Using Real-World Work (The LLM INQUISITOR Field Manual) Operational manual for long form behavioural evaluation. Details procedures for detecting drift, collapse, inconsistency and instability across extended interaction sequences and workflows. https://leanpub.com/conversational-ai-testing Vectored Conversational AI Testing Operational manual for multi vector conversational stability evaluation. Defines procedures for behavioural testing across varied conversational vectors under demanding realistic operational conditions. https://leanpub.com/llm-inquisitor
- Vectored Conversational AI Testing Zenodo: https://zenodo.org/records/21045949 Defines the vectored conversational behavioural evaluation method. Uses controlled conversational variation to observe coherence, boundary handling, context retention and behavioural stability. Establishes the method's relevance to EU AI Act expectations and clarifies the operational boundaries of the approach. LLM INQUISITOR Methodology (GitHub Edition) v1.1 Zenodo: https://zenodo.org/records/20435494 Defines the LLM INQUISITOR methodology: a structured, repeatable discipline for evaluating the behaviour of large language models under controlled load. Provides a formal approach for assessing reliability through observable behaviour and evidentiary traceability. Supports rigorous evaluation in research, safety and enterprise assurance contexts. Argo AI Testing Protocol: Sustained Multi Axis Load Testing Zenodo: https://zenodo.org/records/19919031 Introduces the Argo AI Testing Protocol, a conceptual approach for evaluating AI systems within the User Interaction Space — the full set of observable outputs and interactions available to a user. Addresses the limitations of short, prompt based tests and emphasises extended interaction, shifting user intent and cumulative context effects. Argo's Fundamentals of Failings in Prompt Test Design and Evaluation for LLMs Zenodo: https://zenodo.org/records/19599444 Identifies the core structural failings in prompt test design and evaluation for LLMs. Shows how current methods mismeasure capability, misinterpret outputs and often generate failure states created by the tests themselves. Describes how inconsistent methods emerged in a rapidly expanding industry lacking standards. Argo Prompting: Pattern Formation in LLMs Under Sustained Conceptual Pressure Zenodo: https://zenodo.org/records/19511615 Introduces Argo Prompting, a method for inducing pattern formation behaviour in large language models through sustained conceptual pressure. Distinguishes pattern formation from collapse, hallucination and surface level pattern matching. Provides a practical framework for studying LLM behaviour under extended reasoning conditions.