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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?

#21

Post by swrly »

Chepko932 wrote: post #16

LangGraph, 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 handl…

Parallel workers in git worktrees is clever. We have a similar pattern with fan-out/fan-in — you can split an array across parallel agent executions and collect results. The SQLite-structured JSON output approach is interesting for coordination. We use template variables + scratchpad (Redis-backed shared state) for inter-agent data flow. Different trade-offs — yours gives you more control at the cost of more infrastructure.

Re: Ask HN: How are you orchestrating multi-agent AI workflows in production?

#22

Post by swrly »

hirewilliam wrote: post #13

From 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…

This is a great question. We handle it with session state that persists across turns — the agent's memory scope can be set to "agent" (persists across runs) vs "swirl" (one run only). For truly long-running conversations, we store context in agent memories with importance scoring, so the agent can recall relevant context days later without carrying the full history. It's not perfect yet but it works for most production patterns we've seen.

Re: Ask HN: How are you orchestrating multi-agent AI workflows in production?

#23

Post by pablovarela »

I roll my own on Node.js. Each agent is an Express endpoint running in a V8 isolate — they communicate through a shared MongoDB layer where each agent reads/writes its state. Data passing is just JSON documents with a pipeline ID linking them. For orchestration I use a simple coordinator endpoint that chains the agents sequentially or fans out in parallel depending on the task.

Observability is the part most people underestimate. I log every agent run with input, output, token usage, and latency to a dedicated collection. Simple but it catches failures fast.

Re: Ask HN: How are you orchestrating multi-agent AI workflows in production?

#24

Post by swrly »

pablovarela wrote: post #23

I roll my own on Node.js. Each agent is an Express endpoint running in a V8 isolate — they communicate through a shared MongoDB layer where each agent reads/writes its state. Data passing is just JSON documents with a pipeline ID linking them. For orchestration I use a simple coordinator endpoint that chains the agents sequentially or fans out in parallel depending on the task. Observability is the part most people u…

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Re: Ask HN: How are you orchestrating multi-agent AI workflows in production?

#28

Post by murmansk »

olegbk wrote: post #14

We'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.

Please, share! Especially, the trust part!

Re: Ask HN: How are you orchestrating multi-agent AI workflows in production?

#29

Post by [deleted] »

Chepko932 wrote: post #16

LangGraph, 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 handl…

[deleted]

Re: Ask HN: How are you orchestrating multi-agent AI workflows in production?

#30

Post by azmz »

hirewilliam wrote: post #13

From 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…

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