NETJERU ; A Multi-Stage, Multi-Agent Intelligence Analysis Framework with Adversarial Epistemological Controls

The deployment of large language models (LLMs) in consequential analytical domains — geopolitical risk, conflict early warning, crisis monitoring — exposes a structural gap between model capability and analytical reliability. General-purpose AI systems are subject to well-documented epistemic failure modes, including overconfident scoring, rhetorical inflation, causal reversal, and lagging-indicator misclassification [3, 9, 21]. These failures are not incidental; they are artifacts of single-model architectures in which no structural mechanism exists to challenge, correct, or falsify model outputs before they are accepted as conclusions. This paper introduces NETJERU, a multi-agent intelligence analysis framework that addresses this gap through epistemic function separation, code-level constraint enforcement, and a mandatory human approval checkpoint between analytical and judgment phases. NETJERU routes each analytical query through a sequenced council of seven functionally distinct agents — Isis, Thoth, Set, Osiris, Anubis, Horus, and Ra — each named for an Egyptian deity whose mythological role reflects its analytical function. Agents are constrained by the Dynamic Systemic Stress Matrix (DSSM), a four-dimensional scoring model assessing Symbolic Regulation Failure (SRF), Institutional Collapse (IC), AI Governance Fragmentation (AGF), and Macro Symbolic Shift (MSS) [20]. The system's key architectural innovations include an adversarial self-audit layer (Set) with algorithmically enforced challenge strength caps, structural fingerprint retrieval for historical analogues (Osiris), a mandatory human approval gate (Horus) that cannot be bypassed in code, and a falsifiability requirement at the final judgment stage (Ra) [15]. Critical constraints are implemented at the code level rather than the prompt level, ensuring they cannot be overridden by model output. NETJERU represents a principled approach to human-AI collaborative analysis in high-stakes domains, treating AI failure modes as first-class system concerns rather than afterthoughts.