# Beyond Prompt Chaining: The TB-CSPN Architecture for
## 8 Conclusions and Future Work  
This paper demonstrates the need to shift beyond prompt chaining toward principled architectures for agentic AI. While popular frameworks such as LangGraph and AutoGen simulate agency through LLM orchestration, they fundamentally lack the autonomy, persistence, and formal coordination capabilities required for genuine multi-agent systems. We introduce the TB-CSPN framework as a formal, token-driven alternative that enables modular, semantically grounded, multi-agent collaboration. TB-CSPN integrates LLM-based topic extraction with Colored Petri Nets to support hierarchical agents (supervisors, consultants, and workers) that interact through traceable, interpretable tokens rather than brittle prompt chains. Our empirical evaluation demonstrates TB-CSPN's architectural advantages over LLM-centric pipelines: 62.5% faster processing, 66.7% reduction in API calls, and 166.8% higher throughput while maintaining semantic fidelity. These improvements stem from TB-CSPN's separation of semantic understanding (where LLMs excel) from coordination logic (where formal methods are superior), proving that efficiency gains arise not from avoiding modern AI components, but from using them strategically. The framework's formal foundation enables mathematical verification of coordination properties, comprehensive traceability of decision processes, and seamless integration of human expertise through centaurian architectures. Unlike prompt-dependent systems that conflate semantic processing with orchestration logic, TB-CSPN provides dedicated multi-agent coordination substrate that scales efficiently while preserving interpretability. _Implications for Agentic AI._ Our results demonstrate that genuine agentic behavior necessitates structured communication and compositional design rather than sophisticated tool chaining. The architectural principles validated through TB-CSPN--topic-based semantic interlingua, threshold-driven coordination, and formal verification capabilities--establish a foundation for next-generation agentic systems that combine semantic richness with coordination efficiency. _Future Directions._ Several research directions emerge from this work:  
* _Scalability Analysis_: Investigating TB-CSPN performance across larger agent populations and more complex coordination scenarios. * _Learning Integration_: Developing adaptive topic extraction and threshold optimization mechanisms that maintain formal verification properties. * _Domain Expansion_: Applying TB-CSPN principles to domains beyond financial analysis, including healthcare workflows, emergency response, and scientific collaboration. * _Human-AI Coevolution_: Exploring dynamic role allocation between human and artificial agents based on expertise, context, and trust metrics. * _Standardization_: Developing interoperability protocols for TB-CSPN systems to enable ecosystem-wide adoption. * _Soft Thresholds_: In domains lacking crisp thresholds, fuzzy topic thresholds or probabilistic supervisor rules can be adopted. * _Traceability and Observability_: TB-CSPN's token-level logging enables full traceability; a dashboard interface is under development to support LangSmith-style observability and debugging. As agentic AI evolves from experimental prototypes to production systems, we advocate for architectures that prioritize formal coordination, semantic transparency, and hybrid human-AI collaboration over prompt engineering complexity. TB-CSPN offers a solid foundation for developing transparent, adaptive, and accountable systems capable of addressing real-world challenges requiring both semantic understanding and reliable coordination. The complete implementation, evaluation benchmarks, and documentation are publicly available to enable community validation and extension of these architectural principles, supporting the broader goal of principled agentic AI development. Conceptualization, U.M.B., P.B. and R.P. ; methodology, U.M.B., P.B. and R.P. ; software, P.B. and R.P. ; validation, U.M.B., P.B. and R.P.