In my previous post, I shared a real lesson from building my AI Agent: Functional correctness ≠ Production readiness. The agent was working correctly — RAG, intent routing, evaluations, guardrails and UI were all functioning. Then I hit: 429 — RESOURCE_EXHAUSTED The API quota had been exceeded. That created a new engineering question: What should an AI application do when the model/API temporarily refuses a request? The first solution I implemented For my current evaluation/testing scenario, I introduced a controlled delay using Python's: time.sleep() Instead of continuously firing requests, the test execution pauses between calls. Conceptually: Request → Wait → Request → Wait → Request This helped me avoid sending requests too aggressively during automated evaluation. And it solved the immediate problem in my development/testing environment. #But the bigger #FDE lesson was not time.sleep(). It was understanding rate limiting and resilience. An AI application needs to consider: 🔹 API quotas 🔹 Request frequency 🔹 Retry behavior 🔹 Exponential backoff 🔹 Concurrency 🔹 Caching 🔹 Token consumption 🔹 Monitoring & observability 🔹 Graceful failure / fallback Because at scale, this becomes both a technical problem and a business problem. 💰 Think about it from a customer perspective Imagine an application receiving: 10 requests → Fine 100 requests → More API calls 1,000 requests → Higher token/API consumption 10,000 requests → Quota, latency, concurrency and cost become serious considerations So the FDE question isn't simply: “Can the AI answer the question?” It's: “Can the AI solution continue to provide a reliable customer experience when usage increases or external services become constrained?” That's a completely different level of thinking. My current learning

Your agent can be: ✅ Correct ✅ RAG working ✅ Intent routing working ✅ UI working and still have: ❌ Rate-limit problem ❌ No retry strategy ❌ No caching ❌ No monitoring ❌ No concurrency strategy

Shared this success in the public... please like it... URL: https://lnkd.in/p/dFqNAThT

Happy to share that Agent is Ready and working is as expected... like Evalutaions and Guardrails etc... Concepts Covered: RAG LLM Embedding Chuncks.. Strealist Thanks to @Fde Vision for your support and assistance....

#BuildInPublic. Good News is... got some MD files as Company Docs.... and implementing RAG concepts...there...