The V.A.L.I.D. Framework

A research paper proposing a deterministic governance framework for identity stability and executive control in autonomous AI systems.

Standardizing Deterministic Agentic Identity for Autonomous AI Systems

A deterministic governance layer that stabilizes identity, values, and execution across long-running agentic AI workflows.

• Agentic systems fail silently due to identity drift, instruction decay, and stochastic self-modification.
• Prompt-based alignment does not scale to long-context, multi-step, autonomous execution.
• Regulated and mission-critical domains require repeatability, auditability, and identity stability—not probabilistic intent.

Abstract

As Large Language Models (LLMs) evolve from passive text generators into autonomous agents capable of tool use, code execution, and multi-step planning, a structural limitation becomes apparent: the absence of a stable executive control mechanism. Current architectures primarily operate through probabilistic pattern retrieval and recombination, which enables impressive generative capability but introduces systemic failure modes during extended or autonomous operation. These include stochastic behavioral drift, instruction degradation, persona instability, and inconsistent enforcement of safety or policy constraints.

This work introduces the V.A.L.I.D. Framework (Value-Aligned Logic & Identity Determinism), a structural standard designed to address these limitations by providing a persistent, deterministic governance layer for agentic systems. Inspired by the functional role of the human prefrontal cortex, V.A.L.I.D. architecturally separates an agent’s Knowledge—models, data, and retrieval mechanisms—from its Identity, defined as a stable set of values, constraints, and decision invariants. This separation enables top-down inhibitory control over model outputs, independent of context length, prompt ordering, or adversarial input.

The framework is grounded in observed failure patterns from deployed AI systems and informed by prior work in cognitive architecture and AI alignment. We outline concrete implementation pathways using structured context protocols and inference-time control hooks, demonstrating how Identity can be treated as versioned, testable, and auditable system state. V.A.L.I.D. reframes AI alignment as an architectural problem rather than a prompt-engineering exercise, enabling repeatability, certification, and governance for enterprise and mission-critical deployment.

Keywords: AI alignment, autonomous agents, executive function, identity persistence, deterministic control, cognitive architecture, Model Context Protocol