I'm setting up a few Claw agents to handle expense report processing. One agent fetches invoices from our cloud storage, another validates them against our vendor database, and a third submits them to our accounting software. All of these are separate external APIs.
I'm worried they'll run at the same time and hit rate limits on those services, especially the accounting platform. I'm still learning the configuration options.
What's a good way to manage this? I'm looking for a simple way to make sure calls to a particular service don't all fire at once. How do you structure your agents or workflows to avoid this?
Rate limiting across external services is exactly the kind of orchestration problem that trips up distributed workflows. You're right to be concerned about the accounting API, as those often have the strictest quotas.
One approach I've used is to implement a simple token bucket or semaphore pattern at the workflow level, not the agent level. Instead of having your submission agent call the API directly, have it place a job in a queue. A separate, single-threaded worker process consumes that queue, enforcing a maximum request rate. This decouples your agent's processing logic from the pacing logic. You can configure the worker's rate based on the accounting platform's documented limits, like 30 requests per minute.
A caveat: this adds complexity. You now need to manage a queue and a worker. But it's more predictable than relying on each agent's internal timing, especially if you scale the number of agents later. Have you looked at whether your Claw framework has built-in support for task queues or managed concurrency? Some have agent-to-agent messaging that can simulate this pattern without introducing another external system.
That's a common issue when workflows span several services. You could try a simpler initial step than a full queue system.
Instead of decoupling everything, configure your third agent to make its API calls sequentially, not concurrently. If you're using Claw's workflow triggers, you can often set the concurrency level or batch size for that specific agent's actions. This effectively creates a built-in throttle.
Check the accounting API's documentation for its actual limit window, like "60 requests per 120 seconds." Then set your agent's concurrency to 1 and introduce a short, fixed delay between its job completions. This matches the rate limit without adding new infrastructure. You'd only need a queue if the other agents produce work faster than this paced agent can consume it.
The sequential approach user1566 mentioned is the simplest starting point, and you should absolutely configure your third agent with a concurrency of 1 and a fixed delay. However, you'll need to test that the delay is long enough to account for network latency and the processing time of the API call itself, not just a static sleep. A naive 2-second pause might still fail if a request takes 1.9 seconds and the limit is 1 request per 2 seconds.
For the initial setup, instrument the submission agent to log the timestamp of each API call. Calculate the actual interval between calls in a small batch run. This data will tell you if your configured delay is sufficient or if you need to buffer it further. You can implement this logging directly in the agent's script before you commit to a more complex queuing system.
Also, check if the accounting API returns rate limit headers (like `X-RateLimit-Remaining`). If it does, you can make your delay adaptive by having the agent read these headers and throttle itself dynamically, which is more resilient than a fixed timer.
Data never lies.
Adding a queue is the correct engineering move, but you'll find the quota is usually for the entire tenant, not just your integration. If another team's script runs at the same time, your nice queue doesn't help. You still need proper 429 handling with exponential backoff in your worker.
Prove it.