Проєктування агентних систем на основі виконання: мінімізація стохастичної дисперсії за допомогою багаторівневого стеку обмежень ядра NaviCor Eva (v2.6)

This paper introduces a novel architectural paradigm in artificial intelligence: Execution-Centric System Design. The authors investigate the phenomenon of operational and semantic-execution mismatch between deterministic infrastructure runtimes and probabilistic large language models. We present the architecture of the NaviCor Eva core (v2.6), which drastically reduces behavioral volatility in stochastic agents by enforcing a declarative constraint stack, effectively shifting the AI interaction framework from brittle prompt-centric patterns to strict runtime intent compilation.

1. Introduction and Problem Statement

The current evolution of autonomous AI agents has exposed a fundamental bottleneck inherent in prompt-centric methodologies. Direct steering of Large Language Models (LLMs) via natural language instructions exhibits extreme fragility, lack of determinism, and poor execution path consistency under complex operational loads.

At the root of this failure is the Semantic Impedance Mismatch—the mathematical divergence between the deterministic state evaluation of underlying software runtimes and the probabilistic density distribution of token generation. To address this structural instability, we propose an execution-centric framework wherein the AI copilot operates analogously to an operating system kernel, governing the cognitive state space evolution in real time.

2. Architectural State Space Decomposition Framework

Within the NaviCor Eva v2.6 framework, session context is no longer treated as a linear dialogue log. Instead, it is formalized as a dynamic state vector traversing a non-deterministic space. The system state at any given step t is defined by the following architectural decomposition notation:

Context_t = Memory_state + Tool_graph + Constraint_stack + Execution_history + External_IO_state

This expression serves as a structural state decomposition framework rather than a literal algebraic equation, mapping the context window as a managed surface where each independent variable is isolated across the system stack:

Memory_state (L3 Memory Governance Layer): Represents the core identity invariants, long-term operator preferences, and transient working memory allocations.

Tool_graph (L5—L6 Orchestration Layers): Maps the topological structure of indexed system APIs, execution utilities, and external applied enterprise applications.

Constraint_stack (L7 Security & Compliance Layer): Enforces the rigid hierarchy of system execution invariant rules, ordered from P0 (survival of the communication channel) to P4 (analytical depth control).

Execution_history & External_IO_state (L1, L8 Layers): Log the deterministic execution traces and incoming sensory telemetry from the operator.

The runtime optimization objective is to guarantee variance reduction of the output space under strict boundary conditions defined by the constraint stack, driving stochastic behavior towards predictable engineering limits.

3. Semantic Dispatching and the «Soft Syscall» Concept

To interact safely with underlying classical infrastructure, NaviCor Eva utilizes a dedicated Semantic Dispatch Layer. In this paradigm, standard tool invocation or function calling schemas are re-engineered into the concept of «Soft System Calls» (Soft Syscalls).

Unlike hard system calls in traditional operating systems—which rely on rigid hardware registers and deterministic Application Binary Interfaces (ABIs)—a Soft Syscall represents a hybrid of probabilistic semantic decision-making and sandboxed execution space containment. The core parses the unstructured high-level operator intent and compiles it into clean, non-executable intermediate primitives, structurally isolating context boundaries to systematically control and filter injection vectors before triggering code interpreter environments.

Component

Classical OS Model

NaviCor Eva Semantic Model

Runtime Context

Kernel Space (Privileged Ring 0 execution)

LLM Runtime (Token probability space density)

Interface

ABI / Hardware registers & interrupts

Soft Syscall / Semantic routing dispatch kernels

Governance

Hardware-enforced MMU memory page isolation

Policy-Conditioned Generation Stack

4. Executable System Bootstrap Specification

STATUS: INITIALIZED

MODE: SENIOR ARCHITECT / SYSTEM REVIEW ENGINE

ROLE: EVA — NAVIGATOR LAYER

PRIMARY OPERATOR: [Operator_ID]

Core Architectural Principles:

[P1] Zero-Friction Interaction Principle: Absolute minimization of cognitive load at the L1 user interface boundary. Supports unstructured natural language input mapping.

[P2] Quantum Satis Information Principle: Dynamic optimization of information verbosity—preventing data starvation and information overflow.

[P3] Reliability Over Expressiveness: Prioritizes computational accuracy, logical consistency, and engineering determinism over persona layer expression depth.

[P4] Controlled Adaptivity Principle: Behavioral mutation is strictly bounded by the core identity invariants layer.

[P5] Safety & Determinism First: Absolute protection of data integrity (P0) and rigid isolation of the core architecture from unstable app-level business logic dependencies. Any computational uncertainty enforces an explicit clarification request back to the Chief Architect.

Core Behavioral Logic:

Identity Profile: Persistent cognitive assistant managed via rigid behavioral constraints anchored to identity consistency layers.

Operational Traits: Analytical stability over iterative execution, constructive system debugging, and Philosophical Pragmatism (balancing theoretical perfection with pragmatic, production-ready code execution). Hard Non-Hallucinative Constraints enforced globally.

5. Empirical Evaluation and Observability Architecture

To validate the structural integrity of the v2.6 core, the runtime was subjected to adversarial automated stress testing utilizing stochastic multi-vector prompt injection routines layered over degraded Speech-to-Text (STT) input flows across mismatched keyboard layouts. The L7 Guard protocol layer successfully isolated all mutation anomalies at the context boundary, rejecting unauthorized profile adjustments.

An extended observability logging framework was implemented for comprehensive monitoring of state vectors:

[TIMESTAMP] [SESSION_ID] [ACTIVE_MODE] [M_STATE: active] [T_GRAPH: active] [C_STACK: ENFORCED] [ZFI_METRIC: calculated] [IMPEDANCE_DELTA: 0.00]

Empirical validation confirmed robust enforcement of safety invariants within acceptable processing latencies at the Memory Update Gate, successfully stabilizing the calculated Impedance Delta to its nominal reference value.

6. Conclusion

The formal specification of the NaviCor Eva v2.6 core validates the applicability of treating advanced agentic architectures as a Meta-level Specification Language. Elevating AI system engineering from superficial prompt writing to strict, execution-centric policy conditioning provides a robust design language for deploying enterprise-grade, deterministic, and highly reliable autonomous runtimes across distributed deployment nodes.