Beyond Black Boxes: Building AI You Can Trust

Author: Denis Avetisyan

New research demonstrates how grounding artificial intelligence in a rich understanding of context can deliver not just accurate results, but demonstrably justified decisions.

Integrating ontological context with neuro-symbolic AI architectures improves the accuracy, coherence, and explainability of agentic systems, enabling verifiable reasoning.

Despite advances in artificial intelligence, ensuring both accuracy and transparency in agentic systems remains a significant challenge. This is addressed in ‘Enabling Ethical AI: A case study in using Ontological Context for Justified Agentic AI Decisions’, which proposes a collaborative human-AI approach leveraging ontological context and neuro-symbolic architectures. The authors demonstrate that building an inspectable semantic layer-populated and validated by domain experts-not only improves response quality and mitigates institutional amnesia, but also moves beyond post-hoc explanation toward genuinely justifiable AI decisions. Could this framework represent a critical step toward building AI systems that are not only intelligent, but also demonstrably trustworthy and accountable?

The Inevitable Fade: Institutional Knowledge and Its Discontents

Organizations are increasingly confronting a significant challenge: the erosion of vital knowledge as experienced employees transition out of the workforce. This phenomenon, termed ‘Institutional Amnesia’, extends beyond simply losing documented procedures; it represents the disappearance of invaluable insights, unwritten rules, and contextual understanding accumulated over years of practice. The departure of these individuals often leaves a void in problem-solving capabilities and hinders the effective onboarding of new personnel, as critical historical context is lost. Consequently, organizations risk diminished efficiency, increased errors, and a weakened ability to adapt to evolving circumstances, ultimately impacting long-term sustainability and competitive advantage. Addressing this requires proactive strategies focused on knowledge capture and preservation, ensuring continuity even as personnel changes occur.

Conventional knowledge management systems frequently struggle to preserve an organization’s most valuable assets – the subtle, experience-based insights often referred to as tacit knowledge. These systems typically prioritize explicit knowledge – documented facts, procedures, and reports – while overlooking the unwritten rules, intuitive understandings, and contextual awareness developed through daily practice. Consequently, critical nuances regarding how things are actually done, what shortcuts are effective, and what unstated assumptions underpin successful outcomes remain uncaptured. This reliance on static documentation creates a brittle knowledge base, vulnerable to disruption when experienced personnel depart, and ultimately hinders an organization’s capacity to adapt and innovate effectively in a dynamic environment.

The erosion of institutional knowledge directly impedes an organization’s capacity for effective decision-making, as critical context and lessons from previous experiences fade with departing personnel. This isn’t merely about recalling facts; it’s the loss of how things work – the unwritten rules, the successful workarounds, and the understanding of why certain approaches succeeded or failed. Consequently, innovation suffers, as teams unknowingly reinvent wheels or pursue strategies previously abandoned for sound reasons. Ultimately, this cumulative loss of knowledge weakens organizational resilience, leaving the entity vulnerable to repeating past errors and less adaptable to unforeseen challenges, hindering its long-term sustainability and competitive advantage.

The erosion of institutional knowledge presents a significant threat to long-term organizational success, as the inability to access past experiences dramatically increases the potential for repeating costly errors. Organizations lacking robust knowledge preservation systems find themselves perpetually reinventing the wheel, losing valuable time and resources addressing problems already solved. More critically, the loss of nuanced understanding – the ‘lessons learned’ from both successes and failures – hinders innovation and proactive problem-solving. This creates a cycle of reactive management, diminishing the capacity to anticipate future challenges or capitalize on emerging opportunities, ultimately impacting an organization’s ability to adapt and thrive in a dynamic environment.

Grounding the Algorithm: The Semantic Layer as Reality Check

Agentic AI, while designed for autonomous operation and adaptability, is fundamentally constrained by the quality and scope of its operational context. These systems do not possess inherent understanding; their performance is directly proportional to the data and knowledge available to them. Without a robust contextual foundation, agentic AI may generate outputs that are factually incorrect, irrelevant to the intended purpose, or inconsistent with established organizational guidelines. Consequently, the efficacy of agentic AI is not determined by its algorithmic sophistication alone, but by its ability to accurately interpret and apply the specific parameters of its operating environment.

The Semantic Layer functions as a formalized system for representing an organization’s collective knowledge, encompassing definitions, relationships, and rules governing data and processes. This layer is not simply a data catalog; it is an explicit and inspectable model of institutional knowledge, enabling AI systems to understand the meaning and context of information. By defining key business terms and their interconnections, the Semantic Layer provides a consistent, unambiguous foundation for AI decision-making. This structured representation contrasts with implicit knowledge embedded in disparate data sources or documentation, and facilitates automated reasoning and validation of AI outputs against established organizational standards.

The Semantic Layer functions as a knowledge base that contextualizes AI decision-making within the specific constraints and requirements of an organization. By explicitly representing institutional policies, established procedures, and relevant historical data, this layer provides AI systems with the necessary grounding to avoid outputs that conflict with organizational standards. This structured representation moves beyond the general knowledge embedded in Large Language Models and anchors AI responses in the specifics of the operating environment, thereby increasing the reliability and appropriateness of AI-driven actions and recommendations. Without this contextual grounding, AI systems risk generating outputs that, while grammatically correct, are practically or legally non-viable within the organization.

Research indicates a substantial performance increase in Large Language Models (LLMs) when provided with structured ontological context. Testing across all model-cycle combinations revealed a 100% improvement rate in both accuracy and coherence of AI responses. Quantitative analysis demonstrated a 25% increase in accuracy and a corresponding 25% increase in coherence when comparing tests utilizing minimal contextual enhancement to those with full ontological grounding. These results suggest that explicit knowledge representation is critical for maximizing the reliability and consistency of LLM outputs.

Justifiable AI: Tracing the Logic, Avoiding the Black Box

The requirement for explainability is fundamental to achieving true artificial intelligence, particularly within agentic systems. Unlike traditional AI models functioning as ‘black boxes’, Agentic AI necessitates the capacity to articulate the rationale behind its decisions. This justification isn’t simply a post-hoc explanation, but an integral component of the decision-making process itself. The ability to trace a decision back to its supporting evidence and underlying reasoning is crucial for verifying accuracy, identifying biases, and building trust in the system’s outputs. Without this capacity for justification, an Agentic AI lacks the characteristics of genuine intelligence and presents challenges for deployment in critical applications where accountability and transparency are paramount.

The Toulmin Argumentation Framework, developed by Stephen Toulmin, offers a structured approach to evaluating the logic of an argument by deconstructing it into its core components. These components include the claim – the assertion being made; grounds – the evidence supporting the claim; the warrant – the reasoning that connects the grounds to the claim; backing – additional support for the warrant; qualifiers – statements indicating the degree of certainty; and rebuttals – potential counterarguments. By explicitly identifying and analyzing each element, the framework facilitates a rigorous assessment of an argument’s validity, addressing potential fallacies and weaknesses in reasoning. This structured approach is particularly valuable in complex systems where transparency and accountability are critical, allowing for a clear audit trail of the logic behind a decision or conclusion.

Deconstructing an AI’s decision-making process into its constituent parts – the claim (the conclusion), the grounds (the supporting evidence), and the warrant (the logical connection between them) – allows for systematic evaluation of reasoning validity. This analytical approach facilitates identification of insufficient or flawed evidence supporting the claim. Specifically, examining the warrant reveals whether the stated grounds logically justify the conclusion; a weak or missing warrant indicates a potential failure in reasoning. This decomposition enables granular assessment, pinpointing exactly where the AI’s logic falters, whether due to data deficiencies, flawed assumptions within the warrant itself, or a disconnect between evidence and conclusion.

Justifiable Agentic AI systems require both a comprehensive Knowledge Graph – a structured representation of facts and relationships – and a dedicated Evidence Framework to assess the validity of their reasoning. This framework systematically evaluates the connections between claims and supporting evidence, ensuring decisions are not arbitrary but grounded in verifiable data. Rigorous statistical analysis has demonstrated the effectiveness of this approach; results indicate a statistically significant improvement in both the accuracy and coherence of AI decisions, with a p-value of less than 0.0001 confirming these gains.

Neuro-Symbolic AI: Bridging the Gap Between Pattern Recognition and Reason

Neuro-Symbolic AI represents a significant departure from traditional artificial intelligence by strategically integrating the strengths of two distinct approaches. Neural networks, renowned for their ability to learn complex patterns from vast datasets, are paired with symbolic reasoning – a method that uses explicit knowledge representation and logical inference. This combination yields systems capable of not only recognizing patterns and making predictions, but also of explaining their reasoning process. Unlike ‘black box’ neural networks, Neuro-Symbolic AI provides a level of transparency crucial for building trust and enabling human oversight. The resulting architecture allows for more robust and adaptable AI, capable of generalizing from limited data and handling situations outside of its initial training – a key advantage for real-world applications demanding both intelligence and interpretability.

The practical implementation of neuro-symbolic AI relies on specialized tools designed to bridge the gap between data and reasoning. Platforms such as OntoKai streamline the creation and ongoing management of Knowledge Graphs – structured representations of information that capture relationships between concepts – providing the foundational knowledge for AI systems. Simultaneously, advancements like Avantra AIR are extending the capabilities of Agentic AI directly within enterprise resource planning (ERP) systems. This integration allows AI agents to not only process data but also to actively participate in and automate complex business processes, moving beyond simple automation to intelligent task execution and proactive problem-solving within the core operational infrastructure of an organization.

Organizations are increasingly leveraging neuro-symbolic AI to move beyond simple automation and tackle genuinely complex operational challenges. This technology doesn’t merely execute pre-programmed instructions; it analyzes situations, draws inferences based on established knowledge, and adapts to changing circumstances – enabling the automation of tasks previously requiring significant human expertise. Consequently, decision-making processes become faster and more accurate, as the AI can synthesize data from various sources and provide clear, reasoned justifications for its recommendations. The result is a demonstrable increase in operational efficiency, allowing businesses to optimize resource allocation, streamline workflows, and ultimately achieve higher levels of productivity and profitability through intelligent automation.

The pursuit of artificial intelligence extends beyond mere performance; a crucial frontier lies in creating systems that are not only intelligent but also understandable and trustworthy. Traditional ‘black box’ AI models, while capable of impressive feats, often lack transparency, making it difficult to discern the reasoning behind their decisions. This opacity hinders adoption, particularly in critical applications where accountability is paramount. Neuro-symbolic AI addresses this challenge by integrating symbolic reasoning with neural networks, producing AI that can articulate how it arrived at a conclusion. This explainability fosters confidence, allowing users to validate outputs, identify potential biases, and ultimately, trust the system’s recommendations – a prerequisite for seamless integration into complex operational workflows and widespread organizational acceptance.

The Collaborative Future: AI Augmenting, Not Replacing, Human Expertise

Agentic AI systems poised for maximum effectiveness will fundamentally rely on a collaborative dynamic between artificial intelligence and human expertise. These systems won’t operate in isolation; instead, the AI will proactively propose structures for organizing knowledge – identifying patterns, suggesting connections, and building initial frameworks. Crucially, these proposals aren’t treated as definitive answers, but rather as hypotheses presented to human experts for rigorous validation, correction, and expansion. This iterative process – AI suggesting, humans refining – isn’t merely about achieving accuracy; it’s about leveraging the unique strengths of both parties. The AI offers speed and the ability to process vast datasets, while human experts bring critical thinking, contextual understanding, and the capacity to recognize nuances that algorithms might miss. The resulting knowledge structures, built through this synergy, will be far more robust, reliable, and adaptable than those created by either intelligence alone.

The synergy between artificial intelligence and human expertise extends beyond simply building a more accurate database of facts. Effective Agentic AI systems, designed for collaborative knowledge creation, excel at capturing tacit institutional knowledge – the unwritten, often unconscious understandings and insights held by experienced individuals within an organization. This crucial information, typically difficult to articulate or document, becomes embedded within the evolving Knowledge Graph as human experts validate, correct, and refine the AI’s proposals. The resulting system doesn’t just amass data; it internalizes the nuanced reasoning, contextual awareness, and best practices that define an organization’s collective intelligence, ensuring that valuable experience isn’t lost and can be consistently applied to future challenges.

Agentic AI systems aren’t envisioned as replacements for human intellect, but rather as entities that refine their capabilities through ongoing interaction with experts. This continuous learning process moves beyond simple data ingestion; the AI actively incorporates feedback, identifying patterns in corrections and nuanced guidance to improve its reasoning and predictive abilities. As human experts validate, correct, and extend the AI’s proposed knowledge structures, the system doesn’t just accumulate information-it develops a deeper understanding of context, ambiguity, and the subtle complexities inherent in real-world challenges. This iterative refinement allows these AI systems to tackle increasingly sophisticated problems, moving from automating routine tasks to assisting with strategic decision-making and fostering innovation by augmenting human expertise.

The convergence of Agentic AI and human expertise promises a future where organizations aren’t simply reactive, but proactively anticipate and navigate disruption. This isn’t merely about automating tasks; it’s about cultivating a dynamic organizational intelligence. Through continuous learning loops, fueled by human feedback and refined knowledge graphs, these systems build an inherent resilience. The ability to rapidly assimilate new information, identify emerging patterns, and adapt strategies becomes deeply embedded within the organizational structure. Consequently, entities embracing this collaborative model are positioned to not only withstand volatile conditions but to consistently outperform competitors, fostering innovation and sustained growth in an increasingly complex world. This represents a shift from static, knowledge-based organizations to agile, learning ecosystems.

The pursuit of ‘justifiable AI decisions,’ as outlined in this paper, feels remarkably familiar. It’s a constant cycle: introduce complexity, then scramble to explain it. This work, attempting to ground agentic AI in ‘ontological context’ and ‘neuro-symbolic architecture,’ simply re-packages existing knowledge representation challenges. One recalls David Hilbert’s assertion: “We must be able to answer the question: what are the ultimate foundations of mathematics?” This paper aims for similar foundations in AI, but one suspects that even with a robust semantic layer, production systems will inevitably reveal unforeseen edge cases. Everything new is just the old thing with worse docs.

So, What Breaks Next?

The pursuit of ‘justifiable’ AI, as outlined in this work, feels remarkably like reinventing the expert system. A semantic layer, knowledge graphs… these aren’t novel concepts, merely rebranded for a generation enamored with large language models. The improved accuracy and explainability are, naturally, positive. But let’s not confuse correlation with resilience. Production, as always, will reveal the edge cases, the brittle assumptions baked into even the most carefully constructed ontology.

The real challenge isn’t building a system that can justify its decisions, but one that justifies them consistently, even when presented with data it wasn’t trained on, or – and this is inevitable – data that actively contradicts its foundational knowledge. Neuro-symbolic approaches offer a veneer of robustness, but the inherent tension between learned patterns and explicit rules will always be a source of friction.

One anticipates a future dominated by ‘justification patching’ – endless refinement of ontological context to address newly discovered failures. The field will likely cycle through phases of optimistic pronouncements followed by pragmatic debugging. Everything new is old again, just renamed and still broken. The question isn’t if this system will fail, but when, and whether the resulting explanation will be more useful than a simple error code.

Original article: https://arxiv.org/pdf/2512.04822.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/