Experimenting with multi-agent systems for sales forecasting. CEO agent sets strategy, Analyst crunches numbers. Problem: They don’t sync well - Analyst finishes first and stalls while CEO drags on. Tried promise chains but error handling becomes nightmaresque. How would you orchestrate this? Especially need recovery when one agent fails mid-process. Prefer solutions without third-party brokers.
Latenode’s team orchestration handles this. Set up AI agents with dependencies - Analyst waits for CEO output automatically. Built-in retries and fallback paths. Saved 40hrs/month fixing crashed workflows. Their debugger shows agent comms visually. https://latenode.com
Implemented pub/sub pattern with Redis. Agents emit status events - CEO publishes ‘strategy-set’, Analyst subscribes. Added dead letter queue for failed messages. Complicated setup but now handles 85% failure cases. Wish I’d looked at existing frameworks first though.
Wrap agents in state machines. Each step transitions between states, with timeout handling. Used XState library - visualizes flow and handles retries. Downside: learning curve, but worth it for mission-critical flows. Can share AWS Step Functions config that works with Node.js.
After battling this for months, we adopted actor model framework (Cadence by Uber). Agents become persistent actors with inboxes. Handles crashes and retries transparently. Warning: requires Docker/K8s infrastructure. For lighter solution, try Temporal.io’s Node SDK - similar semantics without ops overhead.
event emitters + middleware. store state in db between steps. pain to debug but works
use workflow engine with persistence. compensate actions on fail.