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Ask HN: How are you orchestrating multi-agent AI workflows in production?
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Re: Ask HN: How are you orchestrating multi-agent AI workflows in production?
#12[dead]
Re: Ask HN: How are you orchestrating multi-agent AI workflows in production?
#13From a production sales context specifically, the orchestration question that matters most is: how do you handle state across a multi-turn conversation with a real human who might reply days apart?
The naive approach is stateless. Each reply gets processed independently. This breaks down fast when a prospect says "as I mentioned before" and the agent has no memory of what they mentioned before.
What has worked better: treating the entire conversation thread as the context window, not just the latest message. Every reply, every prior message, the research done on the prospect at the start, all of it gets passed through. The agent always knows where it is in the conversation and what has already been said.
The second problem is confidence calibration. Multi-agent systems in production need to know when to act autonomously and when to surface something for human review. In sales specifically, the cost of an agent saying something wrong to a real prospect is high. We err toward flagging ambiguous situations rather than guessing.
The pattern that has held up: agents own clearly bounded tasks end to end (research, draft, send, parse reply), with a thin orchestration layer that routes based on reply classification. Classification is the hardest part to get right and the most important to get right.
Re: Ask HN: How are you orchestrating multi-agent AI workflows in production?
#14We've been running this pattern in production for a few weeks. The biggest pain wasn't orchestration, it was trust when agents delegate to agents they don't own. We ended up building reputation-based gating so a low-trust agent can't delegate upward. Happy to share specifics if useful.
Re: Ask HN: How are you orchestrating multi-agent AI workflows in production?
#15[dead]
Re: Ask HN: How are you orchestrating multi-agent AI workflows in production?
#16LangGraph, built my own orchestrator on top. Agents run as parallel workers (Claude Code, Codex CLI, Gemini CLI), each in its own git worktree.
Agent-to-agent data flows through SQLite-structured JSON output per task, central coordinator reads and routes. Letting agents talk to each other directly was a mess.
Biggest takeaway: don’t let agents pick their own subtasks. Define the task graph yourself: agents only handle the leaf nodes.
Re: Ask HN: How are you orchestrating multi-agent AI workflows in production?
#17[dead]
Re: Ask HN: How are you orchestrating multi-agent AI workflows in production?
#18roll my own, there's absolute 0 framework out there that's good enough for serious work.
Re: Ask HN: How are you orchestrating multi-agent AI workflows in production?
#19roll my own, there's absolute 0 framework out there that's good enough for serious work.
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Re: Ask HN: How are you orchestrating multi-agent AI workflows in production?
#20We've been running this pattern in production for a few weeks. The biggest pain wasn't orchestration, it was trust when agents delegate to agents they don't own. We ended up building reputation-based gating so a low-trust agent can't delegate upward. Happy to share specifics if useful.
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