URL: https://www.linkedin.com/posts/pkl87_fde-scale-automated-share-7495076299967774720-cu_Q/?utm_source=share&utm_medium=member_desktop&rcm=ACoAACr01jQByvOc24VPQ3bbS2VmVTh5YSyUcmo While building my AI Agent, I initially tested it the traditional way: Ask a question → Check the response → Repeat → Decide whether it worked. That approach is fine for a quick demo. But once an AI agent starts evolving, manual testing doesn't hashtag#scale. So I implemented an hashtag#automated testing/hashtag#evaluation approach. Instead of manually asking questions every time, I created a set of test questions with their expected outcomes and allowed the application to execute and evaluate them automatically. What does the flow look like? Test Dataset → Agent → Response → Evaluation → PASS / FAIL The test cases cover different scenarios, including: ✅ Questions the agent should answer using RAG ✅ Questions that require the correct intent routing ✅ Questions where the expected result is Handoff ✅ Questions that should not be answered from unsupported knowledge ✅ Retrieval/evaluation scenarios This changed my perspective. The hashtag#FDE angle 🎯 For an hashtag#FDE, testing isn't just about: “Does the demo work?” It's about: “Can I repeatedly prove that the solution is working as expected?” When we change the prompt, retrieval logic, chunking strategy, model, or guardrails, automated evaluation gives us a repeatable way to understand whether the change improved or degraded the solution.

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

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

Happy to share that... I started working of building agent as per Module-5 from Launchpad Series...