The Hidden Risk of Single-Vendor Multi-Agent AI: Monoculture

Everyone Is Building the Same Thing Browse multi-agent AI articles and a pattern emerges fast. LangGraph with GPT-4o playing three roles. CrewAI where the "researcher," "writer," and "editor" are all the same model. AutoGen orchestrating GPT-4 with GPT-4. The LangGraph "swarm" article making the rounds this week: one model, multiple system prompts, branded as emergent coordination. These are not multi-agent systems. They are one agent with a role-switching UI. That distinction matters for a reason nobody writes about: monoculture . When all your agents share the same model, they share the same blind spots. A Bug That Proves the Point This week I was working on lib-foundation — a shared Bash library used by k3d-manager. A function called _deploy_cluster_resolve_provider had been working correctly for months, or so we thought. It contains a TTY check: if [[ -t 0 && -t 1 ]] ; then provider = " $( _deploy_cluster_prompt_provider ) " The logic: if stdin and stdout are both TTYs, show an int