From Manual Testing to Automated Evaluation — An #FDE Learning

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.

That is especially important for AI systems because outputs aren't always deterministic like traditional application logic.

My key takeaway

Manual testing validates a hashtag#moment.

Automated evaluation validates a hashtag#system.

And this becomes even more important when moving from:

Prototype → Demo → Customer Pilot → Production

My next focus is to make these evaluations more comprehensive by tracking metrics such as:

📌 Accuracy

📌 Retrieval quality

📌 Handoff correctness

📌 Guardrail behavior

📌 Failure patterns