What broke first when you went from one AI agent to several?
Summary
A discussion on the operational challenges that arise when scaling from one AI agent to multiple, including context handoff, auth permissions, duplicated work, and cost tracking.
Similar Articles
how to scale AI agents in production workflows when the underlying business process is broken?
A practitioner shares challenges scaling multi-agent AI systems in production, including dealing with shadow workflows (undocumented Slack threads and spreadsheets), context loss across different systems (ERP to CRM), and cross-departmental ownership issues. They seek advice from others who have navigated these real-world problems.
"At what point does adding another agent actually hurt your system? Asking because my 6-agent pipeline is slower and less reliable than my old 2-agent one
A developer shares real-world experiences with AI orchestration frameworks (LangGraph, CrewAI, AutoGen), noting trade-offs between ease of prototyping and production reliability, and asks the community about handling failures, human-in-the-loop, and token costs.
The hard part of agents is not building one. It is operating five.
The article discusses the operational challenges of running multiple AI agents in production, emphasizing observability, recovery, and session management over the initial development of a single agent.
Where AI agents actually break in real workflows (not demos)
A discussion on where AI agents fail in real workflows, highlighting issues with coordination, reliability under messy inputs, and the challenge of reducing human intervention in production.
Anyone actually running AI agents in production with real users - not demos, not 10 beta testers. What's your stack? And has anyone moved back to traditional code after trying agents in prod - why?
A discussion prompt asking about real-world AI agent deployments with 100+ users, covering tech stacks and scaling issues, plus experiences of moving back to traditional code.