# Beyond Prompt Chaining: The TB-CSPN Architecture for
## 6 Case Study: Topic-Grounded Consultant Agents vs. LLM
### Quantitative Performance Evaluation
#### 6.7.3 Architectural Efficiency Analysis  
The empirical results reveal a fundamental architectural distinction that explains TB-CSPN's efficiency gains. The performance differential stems not from different semantic capabilities, but from how each framework manages multi-agent coordination and process orchestration.  
Dedicated Multi-Agent Environment vs. LLM-Mediated CoordinationTB-CSPN provides a dedicated multi-agent coordination substrate through its formal Petri net foundation and token-based communication model. This enables agents to interact through structured semantic channels while restricting LLM usage to information acquisition tasks where semantic understanding is essential.  
LangGraph, in contrast, lacks a dedicated multi-agent environment and relies on LLMs to perform dual functions: both information acquisition (semantic understanding) and process management (agentic interaction coordination). This architectural choice forces LLM involvement in every coordination step, creating inefficiencies where formal methods would suffice.  
Separation of Concerns: Information vs. CoordinationThe 66.7% reduction in LLM calls achieved by TB-CSPN reflects this architectural principle. Both frameworks require LLM capabilities for semantic information acquisition--topic extraction from unstructured text. However, TB-CSPN delegates coordination responsibilities to its formal substrate, while LangGraph continues to rely on LLM inference for inter-agent communication, decision routing, and process orchestration.  
This separation enables TB-CSPN to leverage LLMs where they excel (semantic understanding) while avoiding their use where formal methods are more appropriate (deterministic coordination logic). The result is a system that maintains semantic richness while achieving coordination efficiency through architectural specialization.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Metric** & **TB-CSPN** & **LangGraph** & **Improvement** \\ \hline Average Processing Time & 0.301 s & 0.802 s & 62.5\% faster \\ Peak Throughput & 199.5 items/min & 74.8 items/min & 166.8\% higher \\ LLM Calls per Item & 1.0 & 3.0 & 66.7\% fewer \\ Success Rate & 100.00\% & 100.0\% & Equal reliability \\ Cost Efficiency & USD X per 1000 items & USD 3X per 1000 items & 66.7\% lower cost \\ \hline \hline \end{tabular}
\end{table}
Table 4: Fair performance comparison: both systems using LLMs appropriately for semantic understanding, isolating architectural coordination differences. Tests were conducted on a local Intel i7-8700 machine with 16 GB RAM and no GPU acceleration.  
Process Orchestration OverheadLangGraph's reliance on LLM-mediated process management introduces latency and variability at each coordination point. Token-based communication in TB-CSPN eliminates this overhead by providing explicit semantic channels that enable direct agent-to-agent interaction without requiring LLM interpretation of coordination intent.  
The performance improvements thus demonstrate the advantages of purpose-built multi-agent architectures over LLM-centric orchestration approaches that conflate information processing with process management responsibilities.  
Scalability ImplicationsAt enterprise scale, TB-CSPN's throughput advantage (199.5 vs. 74.8 items/minute) enables processing of approximately 287,000 vs. 108,000 news items daily, representing a qualitative difference in operational capacity.