Author: HM Shihabuddin Jashory (Shihab) • ORCID: 0009-0003-4163-0092 • Date: November 10, 2026
Autonomous Large Language Model (LLM) agents frequently suffer from cascading hallucinations, premature task termination, context window degradation, and supervisory drift when deployed in long-horizon software engineering, cybersecurity auditing, and multimedia production pipelines. In this paper, we present the Dual-Axis 8-Layer Autonomous Agentic Verification and Governance Architecture (HQAS-8), designed and deployed by HM Shihabuddin Jashory. Axis A enforces four intra-agent deterministic guards inside every agent instance (Zero-Repeat Context Bootstrap, Modular Domain Rules & Skills, Hermes Self-Evolving Lifelong Memory, and Python Pre-Tool/Stop-Hook Termination Blockers). Axis B orchestrates a four-tier inter-agent hierarchy across parallel project pipelines (Worker Agent, 45s–60s Heartbeat Supervisor Agent, Dual-Transcript Chief Auditor Agent, and Cross-Pipeline Supreme Inspector General Agent).
Eliminating Auto-Compaction Amnesia and Unverified Execution in Autonomous LLM Agents: UTF-8 Byte-Bounded Trajectory Digest Synthesis (-98.1% to -99.6% bloat reduction), Anti-Bypass Hook Locks, and 3-Gear Multimodal Quality Gates. View Citation Page | Download PDF
Principal Scientist & Founder: HM Shihabuddin Jashory (Scientist, AI Systems Architect & Offensive AI Security Researcher • ORCID 0009-0003-4163-0092 • Wikidata Q141910532 • CERN Zenodo & OSF MetaArXiv).
AI Evaluation & RLHF Research Co-Lead: MD Rafiul Islam Shubho (Researcher, AI Evaluator, RLHF/SFT Specialist & Speech-to-Speech Quality Auditor • Co-Founder & Managing Director, Vision Flow Group • 1,400+ LLM/S2S evaluation tasks, 98.4% rubric calibration, EF SET 81/100 C2 Proficient qyLFMa).
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