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) transition from passive text generators to autonomous agents capable of tool use, code execution, and multi-step planning, they encounter a critical architectural deficit that this paper terms Executive Dysfunction. Current model architectures rely predominantly on what we characterize as "Limbic" processing—the probabilistic retrieval and recombination of patterns from training data—which leads to systematic failures including stochastic drift, instruction fatigue, persona dissolution, and safety constraint bypass during extended context sessions.

This paper introduces the V.A.L.I.D. Framework (Value-Aligned Logic & Identity Determinism), a structural standard inspired by the human Prefrontal Cortex (PFC) that provides top-down inhibitory control over model outputs. By architecturally decoupling an agent's accumulated "Knowledge" from its governing "Identity," V.A.L.I.D. establishes a deterministic governance layer that persists regardless of context length or adversarial manipulation.

We ground our framework in documented failures of deployed AI systems, review relevant literature in cognitive architecture and AI alignment, and propose concrete implementation pathways via the Model Context Protocol (MCP) and native inference hooks. The V.A.L.I.D. standard represents a paradigm shift in AI alignment—from volatile prompt engineering to transparent, auditable Identity Firmware that can be version-controlled, tested, and certified for enterprise deployment.

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