9h • General discussion
Fixed.... Rate Limit Issue....
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.
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
I started with a simple delay to control request frequency during testing.
The next production-oriented evolution is:
Fixed Delay → Retry with Backoff → Rate Limiting → Caching → Monitoring → Resilient Architecture
This was another great reminder that AI engineering isn't only about prompts and models.
It's also about reliability, scalability, cost and customer experience.
Build it. Break it. Understand why. Improve it.
That's the FDE mindset I'm continuing to develop. 🚀
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Fixed.... Rate Limit Issue....
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