Solving Specification Drift and Contextual Degradation via a Tri-Stream Cognitive Architecture with Non-Lossy Canonical Synthesis in Autonomous AI Agents
Year: 2026
Type: article
Abstract: Long-running conversational AI agents and assistive development models inevitably succumb to contextual degradation, specification drift, and sycophantic decay. Standard Retrieval-Augmented Generation (RAG) paradigms treat dialogue turns, human identity, and technical parameters as an undifferentiated, flat vector repository. This structural limitation induces lossy compression of technical truth, context window pollution, and hallucinatory synthesis of project constraints. This paper introduces the architecture of Blue Synergy CORTEX, an external, persistent cognitive runtime operating via the Model Context Protocol (MCP) and stateful REST gateways. CORTEX establishes an architectural Tri-Stream Memory Boundary, bifurcating context into three distinct operational lifecycles: an evolving User Stream (Cognitive Blueprint), an audited Identity Stream (Executive Calibration with immutable Core Axioms), and an invariant, non-decaying Knowledge Stream (Living Canonical Specifications). To permanently eliminate specification drift, the Knowledge Stream utilizes a Dual-Zone Ingestion Pattern that couples sub-20ms fast-append staging buffers with scheduled, non-destructive canonical refactoring governed by an algorithmic Semantic Invariant Guard. Operational telemetry is protected by a Cognitive Immunity Shield, ensuring runtime tool crashes and apologetic model artifacts are filtered from long-term memory. Context retrieval executes via a deterministic, zero-LLM topological pathway achieving sub-65ms latency. Finally, we formalize the compliance model under the European Union Artificial Intelligence Act (EU AI Act, Regulation 2024/1689, Articles 5, 14, and 15), backed by quantitative benchmark metrics demonstrating 98% factual faithfulness and a 2% hallucination rate.
Author: Novota, Peter
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7221454931
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