Deterministic Multi-Agent Cognition (DAIGS)

Canon² — Trust Layer Research Archive. Autonomous software ecosystems increasingly rely on multiple agents operating in concert: synthetic organisms composed of interacting cells, distributed governance authorities coordinating policy enforcement, and cognitive modules specializing in different reasoning domains. These multi-agent configurations require a cognitive substrate that is deterministic (the same inputs produce the same reasoning conclusions across all executions), certificate-anchored (every cognitive evaluation is cryptographically signed and attributable), and governance-constrained (cognitive operations are bounded by declared policy envelopes). Classical multi-agent architectures—from the BDI model 17 to modern reinforcement learning ensembles—are fundamentally probabilistic: agent decisions depend on stochastic processes that produce different outcomes across executions, rendering forensic reconstruction impossible and governance enforcement unreliable. I present the DAIGS Expansion Architecture, a complete multi-agent cognition framework that extends the Deterministic Arbitration and Inference Graph Substrate 5 with six cognitive primitives: Cognitive Identity (certificate-bound agent identification), Cognitive Boundaries (enforceable limits on agent reasoning scope), Cognitive Constraints (declarative rules that shape inference without modifying the inference engine), Cognitive Proofs (verifiable evidence chains that justify each cognitive conclusion), Cognitive Certificates (cryptographically signed records of cognitive evaluations), and Cognitive Governance (governance-layer oversight of cognitive operations). These primitives integrate with Lume 1 through AST-level cognitive operation canonicalization, with the Trust Layer 3 through certificate-anchored cognition and identity-bound reasoning, with Lume-V 4 through envelope-constrained cognitive operations, with LDIR 12 through multilingual cognitive semantic normalization, with SOR 13 through cell-level, signal-level, homeostasis-level, and organism-level cognition, and with GUPAS 2 through six-layer governance of cognitive autonomy. I formalize six multi-agent cognitive pipelines (Detection, Arbitration, Reasoning, Synchronization, Certificate Issuance, and Multi-Agent Cognition) and identify six failure modes specific to multi-agent cognition (cognitive drift, arbitration collapse, certificate mismatch, multi-agent conflict, drift amplification, and intent inversion), each with defined detection, corrective, and preventive mechanisms. To my knowledge, this paper presents the first complete deterministic multi-agent cognition architecture for certificate-bound natural-language programming ecosystems.