"AI agents fail more often than you might expect. OpenAI's API returns errors at a low but non-trivial rate that fluctuates with load, and Claude's API shows similar behavior. On its own, that doesn't sound alarming. But once a system is making hundreds or thousands of calls, those small percentages turn into a steady stream of failures. A task queue turns those inevitable failures from silent data loss into recoverable work."
"The real issue isn't retry logic by itself. AI agents tend to fan out work: a single user request can trigger multiple LLM calls, database writes, and external API requests. Without orchestration, this quickly leads to race conditions, duplicate processing, and very little visibility into what actually broke. A queue gives you ordering, observability, and the ability to resume or replay operations from any point in the chain."
"A single prompt might use 500 tokens or 50,000, depending on context size. Standard worker pools assume roughly uniform task duration, but LLM calls range from 200ms to 30+ seconds. This variance makes normal parallel processing a bad idea. Rate limits make the problem worse. Most LLM APIs enforce both requests-per-minute and tokens-per-minute limits. Hit either one and you'll start seeing 429 errors that ripple through the rest of the system."
AI APIs return errors at low but non-trivial rates that scale into many failures when systems make hundreds or thousands of calls. Task queues turn inevitable failures from silent data loss into recoverable work. AI agents fan out work across multiple LLM calls, database writes, and external API requests, which without orchestration leads to race conditions, duplicate processing, and poor visibility. Queues provide ordering, observability, and the ability to resume or replay operations. Variable token consumption, unpredictable costs, and wide duration variance make parallel processing ineffective, while rate limits require adaptive throttling to avoid cascading 429 errors.
Read at LogRocket Blog
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