Beyond Prompt Chaining: The TB-CSPN Architecture for Agentic AI

Current agentic AI frameworks such as LangGraph and AutoGen simulate autonomy via sequential prompt chaining but lack true multi-agent coordination architectures. These systems conflate semantic reasoning with orchestration, requiring LLMs at every coordination step and limiting scalability. By contrast, TB-CSPN (Topic-Based Communication Space Petri Net) is a hybrid formal architecture that fundamentally separates semantic processing from coordination logic. Unlike traditional Petri net applications, where the entire system state is encoded within the network structure, TB-CSPN uses Petri nets exclusively for coordination workflow modeling, letting communication and interaction between agents drive semantically rich, topic-based representations. At the same time, unlike first-generation agentic frameworks, here LLMs are confined to topic extraction, with business logic coordination implemented by structured token communication. This hybrid architectural separation preserves human strategic oversight (as supervisors) while delegating consultant and worker roles to LLMs and specialized AI agents, avoiding the state-space explosion typical of monolithic formal systems. Our empirical evaluation shows that TB-CSPN achieves 62.5% faster processing, 66.7% fewer LLM API calls, and 167% higher throughput compared to LangGraph-style orchestration, without sacrificing reliability. Scaling experiments with 10–100 agents reveal sub-linear memory growth (10× efficiency improvement), directly contradicting traditional Petri Net scalability concerns through our semantic-coordination-based architectural separation. These performance gains arise from the hybrid design, where coordination patterns remain constant while semantic spaces scale independently. TB-CSPN demonstrates that efficient agentic AI emerges not by over-relying on modern AI components but by embedding them strategically within a hybrid architecture that combines formal coordination guarantees with semantic flexibility. Our implementation and evaluation methodology are openly available, inviting community validation and extension of these principles.