Deterministic Blackboard Pipelines with Specialized LLM Knowledge Sources: A Generalizable Architecture for Intelligent Multi-Stage Reasoning

Classical blackboard architectures offer powerful shared-state coordination for multi-agent reasoning, but their opportunistic Knowledge Source (KS) firing model is known to create emergent, difficult-to-trace execution that limits practical widespread adoption. This paper proposes a simplification: replace opportunistic KS activation with a deterministic pipeline scheduler, and realize LLM-based KS as a specialized Large Language Model (LLM) prompted for a distinct task specialization role. The resulting architecture, a Deterministic LLM Blackboard Pipeline (DLBP), preserves the core blackboard value proposition of shared state and KS specialization while dramatically reducing coordination complexity. We argue that LLM specialization via prompt engineering is the key enabler of domain portability: the same five-role pipeline skeleton (Normalizer, Proposer, Critic, Verifier, Correlator) is hypothetically mapped across domains (cybersecurity, medical triage, financial fraud detection, legal discovery, and industrial monitoring) by substituting and refining the KS prompts, with minimal pipeline architecture modifications or coordination logic changes. We report operational validation from a cyber situational awareness instantiation, demonstrating per-KS latency profiles, fallback behavior, prompt regression recovery, and cost tradeoffs across specialized LLM roles. An extended case study is available as a companion preprint [1]. The architecture represents a practical convergence of classical AI blackboard principles with modern LLM capabilities, yielding an intelligent systems pattern suitable for many decomposable multi-stage reasoning tasks.