Why We Added Rate Limits Between AI Agents
The article describes how a development team initially applied rate limits only at traditional boundaries—databases, external services, model providers, and public endpoints. However, in production, they discovered that the most critical need for rate limiting was between AI agents themselves. Their architecture involved specialized agents that could call each other to fulfill a user request: document retrieval, classification, validation, summarization, workflow planning, and action execution. During testing, the system worked well because human-like pacing masked the issue. But in production, agents made decisions and triggered follow-up actions almost instantly, causing cascading interactions that multiplied under load. A single user request could spawn a chain of agent calls, leading to unexpected infrastructure strain. The team concluded that rate limits between agents are essential to prevent runaway loops and ensure system stability, even when each agent's individual behavior seems reasonable.
Agent-to-agent rate limits prevent cascading failures in multi-agent systems.