The rapid emergence of large language model (LLM)‑driven agents has led to a flood of protocols, frameworks, and libraries, making it hard for developers to assemble state‑of‑the‑art AI solutions. Without a systematic assessment of how these components interoperate, practitioners encounter challenges such as skill acquisition, governance, and infrastructure even before joining the Agentic Web ecosystem.\ \ To address this gap, we introduce MASTIF (Multi‑Agent System TestIng Framework), a unified platform for evaluating multi‑agent systems. Using the Mind2Web benchmark (300 tasks), we conduct experiments across:\
- 3 mainstream LLMs (e.g., GPT‑4, Claude, Llama 2)\
- 3 communication protocols (REST, WebSocket, gRPC)\
- 4 agent implementation frameworks (LangChain, AutoGPT, CrewAI, OpenAI Function Calling)\ \ Results indicate that a task‑specific technology stack can substantially improve task completion rate, efficiency score, and social‑value stability, while reducing verbosity and time‑to‑completion. For instance, on information‑retrieval tasks, the Llama 2 + gRPC + CrewAI combo outperforms the default setup by ~12% in completion rate and cuts average execution time by 1.8 seconds.\ \ These baseline metrics provide a reference for future system design, assessment, and optimization, and help the community establish standardized evaluation criteria in the fast‑evolving Agentic Web landscape.\ \ Review