How LangGraph and MCP Helped Fix a Multi-Agent AI Feedback Loop

A developer team discovered their AI support bot was trapped in a circular feedback loop, where multiple agents kept querying each other indefinitely instead of resolving customer issues. Investigating the logs revealed the root cause was a multi-agent architecture lacking structured communication boundaries. To fix this, the team adopted LangGraph's StateGraph API and the Model Context Protocol (MCP) to define explicit conversation states and controlled transition conditions. Using the add_conditional_edges method, they enforced rules that determined when and how agents could move between states, breaking the cycle. The experience highlighted the critical importance of carefully designing state transition logic in multi-agent AI systems to prevent runaway loops.