I’m new to building AI agents, but after a lot of research I’ve decided to go all-in on the LangChain ecosystem. My goal is to build a serious AI networking agent focused on troubleshooting and analysis (think real-world network ops, not demos). Some context: Most integrations will be via MCP servers (network devices, APIs, etc.) I don’t see much need for filesystem-based tools The agent should be able to: Run structured troubleshooting workflows Potentially spawn sub-agents for parallel analysis (e.g., per device or hypothesis) Reuse modular “skills” over time Where I’m stuck is how to actually structure this within the ecosystem: Should I start simple with LangChain and evolve later? Jump straight into LangGraph for more control? Or think in terms of deeper agent systems from day one? I also don’t know whether I’m overengineering this vs missing something fundamental. Additional questions: Are there recommended architectural patterns for multi-agent troubleshooting systems? Is a hybrid approach (structured workflow + agent decision-making) common in practice? Any real-world examples of similar systems (infra/networking/observability agents)? Would really appreciate guidance from folks who’ve built non-trivial agent systems. I’m optimizing for something that can scale beyond a prototype. 1 post - 1 participant Read full topic