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Are orchestration frameworks for production or just for getting started?

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calendar_today ago • schedule1 min read

One thing I have been wondering recently:

Do AI orchestration frameworks actually survive in production as systems become more complex?

Most tutorials teach LangGraph, LangChain, CrewAI, Pydantic AI, etc.

But when I look at products like Claude Code, Cursor, Windsurf, OpenAI Codex or Devin I wonder how much of their orchestration is still framework-based versus custom-built.

For engineers working on production AI systems:

What does your orchestration layer look like?
Where do frameworks help?
At what point did you decide to build your own?

I am genuinely curious to hear how others approached this. If you have built AI agents in production, I would love to hear your thoughts and the reasoning behind your architectural decisions.

4 Comments

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Interesting take. I think frameworks are great for getting started, but production is where the real trade-offs show. What's your go-to setup today?

1 vote
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@[Mehadi Hasan] I have worked with Langchain, LangGraph, DSPy, the OpenAI Agents SDK but I am still exploring which approach scales best in production which is why I started this discussion.

1
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Great perspective. Orchestration frameworks are excellent once your workflows become complex, but they’re not always necessary from day one. Starting simple and adding orchestration only when the complexity justifies it can save a lot of unnecessary overhead. Great read.

1 vote
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@[unwind] Totally agree. Starting simple and introducing orchestration only when it is actually needed seems like the most practical approach.

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Ayush Singh
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