Building on Shifting Sands: The Need for Epistemic Rigor in AI-Driven Design

Author: Denis Avetisyan

As artificial intelligence increasingly guides engineering decisions, ensuring the validity and traceability of underlying assumptions is critical to avoid designs built on outdated or unreliable information.

This review proposes a First Principles Framework to manage evidence decay and maintain epistemic accountability in AI-assisted software engineering.

The accelerating pace of AI-assisted software engineering introduces a critical paradox: coding assistants can outpace our ability to validate their decisions. This paper, ‘AI-Assisted Engineering Should Track the Epistemic Status and Temporal Validity of Architectural Decisions’, argues for explicit mechanisms to track the reliability and lifespan of architectural choices, proposing the First Principles Framework (FPF) to manage ‘epistemic drift’. By formalizing requirements for epistemic accountability-including layered knowledge representation, conservative assurance aggregation grounded in fuzzy logic, and automated evidence decay tracking-the authors demonstrate that a significant percentage of decisions can quickly become based on stale evidence. Will these techniques enable truly trustworthy and adaptive AI-driven engineering systems, or will we continue to build upon foundations of unverified assumptions?

The Inevitable Erosion of Trust in Automated Systems

The accelerating pace of AI-assisted software engineering, while promising increased development speed, frequently introduces vulnerabilities due to compromised validation processes. Systems built with this approach often prioritize rapid iteration over thorough testing and documentation, resulting in what are termed ‘brittle’ systems – those susceptible to unexpected failures even with minor changes. This isn’t necessarily a flaw in the AI itself, but rather a consequence of the development lifecycle adapting to maximize output. Traditional, comprehensive validation steps are often curtailed or automated with less stringent criteria, creating a trade-off between speed and robustness. Consequently, these systems can accumulate hidden technical debt and exhibit unpredictable behavior under real-world conditions, potentially requiring significant rework and undermining long-term maintainability despite initial gains in velocity.

Current documentation practices for software architecture, such as Architectural Decision Records (ADRs), frequently fall short in a rapidly evolving technological landscape. While ADRs excel at capturing the initial rationale behind design choices, they typically lack the mechanisms to track how, and when, that reasoning becomes outdated. This presents a significant challenge, as the context informing a decision – dependencies, external APIs, or even core assumptions – can shift dramatically over time. Without a system for versioning or flagging the temporal validity of claims within ADRs, teams struggle to ascertain whether a previously sound decision remains appropriate, potentially leading to the propagation of flawed logic and increased technical debt. This absence of ‘living documentation’ effectively creates a knowledge decay problem, where valuable insights are lost as the system evolves, hindering effective maintenance and innovation.

A concerning trend is emerging in software development where the pace of decision-making, accelerated by artificial intelligence, increasingly outstrips the ability to maintain supporting evidence. Recent retrospective analysis of two internal projects demonstrates a significant erosion of trust in architectural decisions, revealing that between 20 and 25 percent of those decisions were based on stale or invalid evidence within a mere two months. This rapid obsolescence of rationale suggests a growing ‘trust gap’ – a disconnect between the speed of AI-assisted choices and the reliable validation of those choices – which poses risks to system stability and long-term maintainability. The findings highlight a critical need for improved mechanisms to track the temporal validity of claims and ensure the continued relevance of supporting information within the software development lifecycle.

First Principles: A Framework for Epistemic Accountability

The First Principles Framework is a formalized system designed to assess the reliability of decisions made by Artificial Intelligence. This system moves beyond simple probability-based confidence scores by establishing a rigorous, logically-structured approach to evaluating AI reasoning. It requires the explicit articulation of all premises, assumptions, and logical steps involved in an AI’s decision-making process. Each element within this chain of reasoning is then subject to individual scrutiny, allowing for the identification of potential vulnerabilities and biases. The framework’s formal nature enables consistent and auditable evaluations, crucial for applications demanding high levels of accountability, such as legal, medical, or financial systems. By grounding assessments in first principles – foundational truths considered self-evident – the framework aims to provide a more robust and transparent method for determining the trustworthiness of AI outputs.

The First Principles Framework utilizes Fuzzy Logic to represent degrees of truth and confidence, moving beyond strict binary evaluations. This allows for the modeling of imprecise or incomplete information inherent in AI systems and their data. Formality Levels are then assigned to claims, indicating the level of evidence and rigor supporting them; these levels are not absolute but rather reflect a spectrum of validation, ranging from informal hypotheses to formally proven statements. A claim’s overall confidence is thus expressed not as a single value, but as a fuzzy set representing the distribution of possible truth values given the available evidence and its associated Formality Level. This allows for nuanced representation of uncertainty, acknowledging that AI-driven conclusions are rarely definitively true or false, and enabling a more realistic assessment of their reliability.

The Weakest Link Upper Bound principle, central to this framework, dictates that the overall confidence score assigned to an AI-driven conclusion is fundamentally limited by the validity of its least reliable supporting evidence. This means that if a decision relies on multiple lines of reasoning, each with an associated confidence level, the maximum achievable confidence for the final conclusion is equal to the minimum confidence among those supporting arguments. Mathematically, this can be expressed as: $Confidence_{overall} = min(Confidence_1, Confidence_2, ..., Confidence_n)$ , where ‘n’ represents the total number of supporting arguments. This principle prevents overestimation of reliability and ensures a conservative assessment of AI decision-making, acknowledging that the entire chain of reasoning is only as strong as its weakest link.

Validating Claims: A Rigorous Assessment Methodology

The ADI Cycle, central to this framework, provides a structured approach to knowledge claim validation. Abduction initiates the process by generating plausible hypotheses based on initial observations or data. These hypotheses are then subjected to deductive reasoning, where logical consequences are derived and tested against existing knowledge or further data. Finally, induction generalizes findings from specific instances to broader claims, refining the initial hypotheses. This iterative Abduction-Deduction-Induction cycle ensures a systematic and rigorous evaluation of knowledge claims, allowing for continuous refinement and increasing confidence in the resulting conclusions. The process is not strictly linear; findings from later stages often necessitate revisiting earlier stages to refine hypotheses or explore alternative explanations.

Formal methods, encompassing techniques like model checking and theorem proving, are employed to establish system correctness through mathematical proofs of adherence to specified properties. Complementing this, property-based testing automatically generates numerous test cases to verify that these properties hold across a wide range of inputs and states. This integrated approach moves beyond traditional unit testing by focusing on what the system should do, rather than how it does it, and leverages mathematical rigor to provide verifiable guarantees that the system behaves as intended, identifying potential errors that may not be revealed by conventional testing methods. The combination increases confidence in system reliability and safety-critical applications, reducing the potential for runtime failures due to logical errors.

The Gödel T-norm, utilized within the framework for evidence aggregation, operates as a conservative function ensuring that the overall confidence level is limited by the weakest supporting evidence. Mathematically, the T-norm calculates the intersection of evidence strengths; specifically, $T(a, b) = \min(a, b)$ , where ‘a’ and ‘b’ represent the confidence values of individual claims. This approach avoids the potential for overconfident conclusions that can arise from functions which amplify weaker signals or allow for the propagation of unsubstantiated assertions. By prioritizing constraint over amplification, the Gödel T-norm effectively minimizes the risk of accepting claims based on insufficient or unreliable data, thus promoting a more robust and cautious validation process.

Managing Epistemic Decay: Preserving System Integrity Over Time

The reliability of any knowledge system hinges on acknowledging that information isn’t static; evidence decays over time. This framework directly addresses this phenomenon by mandating periodic re-validation of all claims, or their eventual deprecation if supporting evidence weakens. This isn’t simply about correcting errors, but about proactively mitigating risk-recognizing that assumptions valid today may become inaccurate as new data emerges or contexts shift. By systematically tracking the strength of evidence, the system avoids perpetuating decisions based on outdated information, ensuring a continually refined and trustworthy knowledge base. The alternative-allowing claims to linger unsupported-results in a significant accumulation of ‘stale’ decisions, discovered only during critical incidents or costly refactoring processes.

A robust framework for artificial intelligence necessitates careful consideration of scope congruence – the principle that supporting evidence must align with the specific context of a decision. Without this alignment, even accurate data can lead to misinterpretations and flawed conclusions. Establishing levels of scope congruence allows a system to assess whether a particular piece of evidence remains relevant and applicable to the current situation, effectively filtering out outdated or improperly contextualized information. This proactive approach minimizes the risk of acting on stale data, ensuring that AI-driven decisions are grounded in the most appropriate and reliable knowledge base available. The system then prioritizes evidence based on its contextual relevance, thereby bolstering the accuracy and trustworthiness of its outputs.

A system’s long-term reliability hinges on actively addressing the erosion of knowledge – a phenomenon known as epistemic decay – rather than reacting to its consequences. Current practices reveal a stark disparity: fully 86% of decisions based on outdated information are only identified during critical incidents or during costly refactoring processes. However, implementing a framework for flagging potentially flawed premises – known as FPF tracking – shifts this paradigm, allowing for the proactive identification of 14% of these stale decisions before they impact system performance or user trust. This transition from reactive troubleshooting to preventative maintenance is not merely an efficiency gain, but a fundamental step toward building AI systems capable of sustained, dependable operation and fostering confidence in their outputs.

Towards Trustworthy AI: Extending the Framework’s Reach

The Machine Configuration Protocol represents a significant advancement in aligning artificial intelligence with verifiable reliability. It achieves this by extending the existing framework to directly incorporate epistemic tracking – the ability to monitor and record the confidence levels associated with each step of an AI’s reasoning – with Large Language Model (LLM) Coding Assistants. This integration isn’t merely about adding a log; it’s about building a system where every line of code suggested, every modification made, is accompanied by a clear record of why the AI made that decision and how confident it is in its correctness. The protocol facilitates a continuous audit trail, allowing developers to trace the origins of code, understand the AI’s thought process, and ultimately, validate the integrity of the software being produced. This seamless connection between LLM assistance and epistemic rigor paves the way for a new era of transparent and trustworthy AI-driven software engineering.

The integration of epistemic tracking with Large Language Model (LLM) Coding Assistants facilitates a powerful synergy between artificial intelligence and software development best practices. Developers are now positioned to harness the efficiency and creative potential of AI-driven code generation without sacrificing accountability. This is achieved by automatically documenting the reasoning behind each suggestion or code alteration, creating a detailed and verifiable audit trail. Consequently, every line of code, every algorithmic choice, and every debugging step can be traced back to its origin, fostering transparency and enabling rigorous validation. This detailed record not only supports debugging and maintenance but also allows for independent review and certification, ultimately building confidence in the reliability and security of AI-assisted software systems.

The future of software engineering hinges on establishing systems that are not merely capable, but reliably so. Prioritizing epistemic rigor – a commitment to justified, true belief in the foundations of code – is paramount to achieving this. This approach moves beyond simply generating functional code with AI assistance; it focuses on creating a verifiable chain of evidence that demonstrates why a system behaves as it does. Such transparency allows developers to confidently integrate AI-generated components, knowing that potential errors or biases can be traced and rectified. Ultimately, this pursuit of demonstrably trustworthy AI unlocks the full potential of AI-assisted software engineering, fostering innovation while ensuring the creation of robust and dependable systems for critical applications.

The pursuit of robust AI-assisted engineering, as detailed in this work, inherently grapples with the relentless march of time and the decay of knowledge. This echoes Marvin Minsky’s observation: “You can’t expect to understand something if you’re not willing to be surprised.” The First Principles Framework (FPF) proposed aims to mitigate ‘epistemic drift’ – the gradual erosion of confidence in architectural decisions – by formally tracking evidence validity. This isn’t merely about preserving information; it’s acknowledging that all systems, even those underpinned by formal methods, are subject to temporal degradation. Acknowledging this inherent fragility is crucial for building resilient and trustworthy engineering systems, preventing the subtle accumulation of stale assumptions that ultimately compromise assurance aggregation.

What Lies Ahead?

The First Principles Framework, as presented, addresses a fundamental truth of engineered systems: all knowledge has a half-life. Versioning is a form of memory, certainly, but memory fades. The challenge isn’t simply recording architectural decisions, but discerning their continued validity in the face of epistemic drift. The paper rightly points toward assurance aggregation, yet the very act of aggregation introduces a new decay vector – the reliability of the aggregated sources themselves. This suggests future work must move beyond merely tracking evidence, and begin modeling the rate of evidence decay, perhaps borrowing from concepts of statistical thermodynamics or information theory.

A critical, and largely unresolved, problem lies in the automation of ‘forgetting’. AI assistance, ironically, may accelerate the propagation of stale knowledge if not explicitly programmed to recognize its own limitations. The arrow of time always points toward refactoring, but an intelligent system must not only perform refactoring, but also justify it – a task demanding a nuanced understanding of both technical debt and the evolving epistemic landscape.

Ultimately, this line of inquiry isn’t about building ‘safe’ AI, but about building systems that age gracefully. The pursuit of absolute assurance is a mirage. The real goal is to create engineering practices that acknowledge uncertainty, embrace obsolescence, and prioritize the ongoing maintenance of a reliable knowledge base – a task less about conquering complexity, and more about accepting it.

Original article: https://arxiv.org/pdf/2601.21116.pdf

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