# Beyond Prompt Chaining: The TB-CSPN Architecture for
## 1 Introduction  
Current \"agentic AI\" frameworks like LangGraph, AutoGen, and OpenAI's multi-agent examples promise autonomous agents capable of planning, reasoning, and coordinating. However, these systems achieve coordination through sequential prompt chainingand centralized LLM orchestration--approaches that fundamentally limit scalability and autonomy. They lack persistent memory, goal continuity, and principled inter-agent communication, instead relying on brittle prompt engineering and implicit state management.  
The core limitation is architectural: these frameworks conflate semantic understanding with process orchestration. Every coordination decision requires LLM inference, creating bottlenecks where formal methods would be more appropriate. This \"LLM-mediated coordination\" approach forces expensive language model calls for routine process management tasks, limiting both performance and predictability.  
Recent industry efforts recognize these limitations. Google's Agent-to-Agent (A2A) Protocol ([[https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/](https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/)]([https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/](https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/)), accessed on 27 June 2025) enables structured interaction between AI agents through standardized communication layers, while the Model Context Protocol (MCP) ([[https://modelcontextprotocol.io/introduction](https://modelcontextprotocol.io/introduction)]([https://modelcontextprotocol.io/introduction](https://modelcontextprotocol.io/introduction)), accessed on 1 July 2025) addresses vertical integration with tools and resources. However, [PERSON] and [PERSON] [1] demonstrate that combining these protocols introduces critical challenges, including semantic mismatches, security risks, and orchestration complexity that exceed current capabilities.  
We present TB-CSPN, a formal architecture that addresses these limitations through _architectural separation_: semantic processing occurs through strategic LLM usage while coordination employs rule-based business logic. This separation enables TB-CSPN to achieve 62.5% faster processing and 66.7% fewer LLM calls than LangGraph-style orchestration while maintaining semantic fidelity.  
TB-CSPN's key innovation is a dedicated multi-agent coordination substrate built on Colored Petri Net semantics. Instead of LLM-mediated process management, agents communicate through structured topic-annotated tokens that enable formal verification of coordination properties. The framework supports three distinct agent roles: LLM-powered consultants for semantic understanding, human supervisors for strategic oversight, and specialized AI workers for operational execution.  
Our empirical evaluation demonstrates that efficient agentic AI emerges not from avoiding modern AI components, but from using them strategically within architectures designed for multi-agent coordination. TB-CSPN shows how formal methods can complement rather than compete with LLM capabilities, providing a foundation for scalable, verifiable, and human-controllable agent systems.  
**Paper Organization.** Section 2 reviews traditional multi-agent systems and recent LLM-native frameworks. Section 3 establishes minimal criteria for agency and explains why current LLM pipelines fail to meet them. Section 4 presents the TB-CSPN architecture and its formal foundations. Section 5 details our implementation including the multi-engine system supporting rule-based coordination, Petri net semantics, and LLM integration. Section 6 provides a comprehensive case study with quantitative evaluation showing TB-CSPN's advantages over LangGraph-style coordination. Section 7 presents systematic comparisons with leading frameworks. Section 8 concludes with implications for next-generation agentic AI architectures.