A rate limit can feel like the system is broken. Usually it’s the system protecting itself. APIs, AI models, and web services can only handle so much at once, so they cap usage to keep one user, app, or agent from flooding the service and making it worse for everyone.
Think about a deli counter with a ticket machine. You take a number and wait your turn. The counter may serve sixty people an hour. If two hundred rush at once, the deli doesn’t magically get faster. It makes people wait, or stops taking new orders for a bit.
How it shows up
You’ll see rate limits with an API, a model provider, a connector, or a tool an agent uses. The cap might be requests per minute, tokens per minute, or daily usage. If an agent is pulling ten thousand records or retrying a failed command too quickly, it can hit the limit, and the service may return an error, slow down, or tell it to wait. This is where queues help: line up the work and process it at the pace the system allows. Rate limits also connect to latency. If your workflow keeps waiting between requests, the job feels slow, but the agent is simply waiting its turn. Token limits feel similar, though a token limit counts how much text the model can process while a rate limit counts how fast you can send work.
Why you care
Real systems have traffic rules. Ignore them and your workflow gets flaky at the exact moment you need it dependable. The practical move is to design workflows that respect the counter: batch where it makes sense, retry politely, spread work out, and don’t make an agent hammer a service just because it can loop quickly. Good agent work knows when to wait its turn.