Your AI Agent Will Fail in Production Without a Reliability Layer
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来源：https://dev.to/abdul___rehman/your-ai-agent-will-fail-in-production-without-a-reliability-layer-47k7
I spent months building an LLM scoring pipeline that processed 10,000 job listings a day. It worked beautifully in staging. Then it hit production and the bills started climbing fast.
The problem wasn't the model. The problem was that I had built a demo, not a production system. The gap between "it works" and "it works reliably at scale" is where most AI agent projects die.
My first mistake was treating the OpenAI API like a utility. I sent prompts, got responses, moved on. No tracking. No budgets. No cost-per-request visibility. A few weeks in, I checked the billing dashboard and saw a number that made me rethink the architecture entirely.
I fixed it with two changes. First, I routed all batch processing through OpenAI's Batch API — much cheaper, handles the same throughput with a few hours of latency. Second, I added model routing based on task complexity. Simple classification goes to GPT-4o mini at a fraction of the cost. Complex reasoning stays on GPT-4.
LLM APIs fail. Not often, but when they do, it's at the worst possible moment. The naive approach is to catch the error and retry immediately. That's how you get a thundering herd problem. I switched to exponential backoff with jitter — each retry waits longer, with a random offset to spread the load.
Most people think of function calling as a way to let the LLM take actions. I think of it as a way to constrain what the LLM can output. Function calling with a strict JSON schema turned the model's output into something I could parse and validate before it touched the rest of the system. The schema acts as a contract. If the model can't produce valid output, the call fails fast instead of polluting the database with garbage.
You can't fix what you can't see. I wired Sentry for error tracking. But the real value came from adding structured logging to every LLM call — model used, prompt hash, response time, token count, result, and any errors.
Most AI products ship without any of this. They work in demos because demos don't have 10,000 concurrent requests or unpredictable API behavior. If you're a founder shipping an AI feature, your competitors are probably cutting corners on reliability. That means you can win by doing the boring work. It's not glamorous. But it's the difference between a product that works and a product that works consistently enough that people trust it.
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看了 demo 视频，用户体验做得不错。