๐ง๐ต๐ฒ ๐จ๐ป๐ฒ๐ ๐ฝ๐ฒ๐ฐ๐๐ฒ๐ฑ ๐ก๐ฒ๐ฒ๐ฑ ๐ณ๐ผ๐ฟ ๐ฅ๐ฎ๐๐ฒ ๐๐ถ๐บ๐ถ๐๐ ๐๐ฒ๐๐๐ฒ๐ฒ๐ป ๐๐ ๐๐ด๐ฒ๐ป๐๐ Most developers think about rate limits at API boundaries to protect databases, external services, and public endpoints.
- Protect the database
- Protect external services
- Protect model providers
- Protect public endpoints This is standard infrastructure design. What surprised us was the need for rate limits between AI agents, not between users and agents, but between agents themselves.
Our workflows started simply: one agent handled a task, and if it needed more information, it called another specialized agent. The architecture looked clean, with responsibilities separated and each agent having a focused purpose. The system worked well during testing, but when we put it into production, we encountered unexpected infrastructure pressure. This was not because of increased user traffic, but because agents increased interactions with each other.
A single user request could trigger multiple actions, such as document retrieval, classification, and validation. Each step might involve additional agent interactions, which multiplied quickly under load. We noticed amplification, where a single request could generate dozens of internal requests. For example:
- Agent A requests context
- Agent B requests additional context
- Agent C validates information
- Agent D performs verification
The most dangerous issue was feedback loops, where agents developed interaction patterns that continuously requested information from each other. This created significant waste, including repeated validation cycles and duplicate retrieval requests. To address this, we introduced internal rate limits between agent workflows to control requests per workflow, agent interaction frequency, and retry volume. The goal was not to restrict agents, but to prevent runaway behavior and keep the system predictable.
By introducing rate limits, we made inefficient workflows obvious and discovered redundant agent responsibilities and unnecessary validation stages. Rate limits acted like a diagnostic tool, exposing inefficiencies that would otherwise remain invisible. AI systems need the same discipline as traditional distributed systems, with every service operating within limits and every resource having constraints. Without limits, complexity grows faster than expected, and complexity eventually becomes operational risk.
Source: https://dev.to/karan2598/why-we-added-rate-limits-between-ai-agents-ogh Optional learning community: https://t.me/GyaanSetuAi