Stop Vibe-Testing Your Agents — It’s Time to Benchmark Like a Pro

If you’re still manually testing your AI agents, you’re throwing away time, money, and customers. “Vibe testing” — that gut-check process where you poke at your agent to see if it feels right — is a trap. It’s subjective, inconsistent, and a fast track to building agents that underperform. Your customers don’t care about your vibes; they care about results. Stop losing them because your agent sucks. There’s a better way: benchmarking. Whether you use existing benchmarks or craft your own, it’s time to level up. And guess what? We’ve built a platform to make it dead simple.

Case Study: Building a Codebase Assistant

A while back, we built a codebase assistant to help new developers onboard faster. The idea was simple: give developers a tool that could navigate a large codebase, answer technical questions, and provide context-aware guidance — like a supercharged internal wiki.

Here’s how it worked:

• Connect and Parse: We’d hook up a GitHub repo, and the agent would download the code. Using tree-sitter parsers, it would scan each file to identify key components like functions, classes, and modules.
• Segment and Embed: The code was then broken into meaningful chunks. Each chunk was turned into a vector using an embedding model and stored in a vector database.
• Search and Refine: When someone asked a question, the agent would use k-nearest neighbors (KNN) search to retrieve the most relevant code snippets.
• Answer Generation: Those snippets — along with the question — were passed to a large language model (LLM) to generate a natural language response.

We made it. But very soon after, we fell into the vibe testing trap.

The Vibe Testing Trap

At first, vibe testing felt fine. We’d ask the assistant a few questions, maybe bring in a teammate or two. If the answers sounded good, we’d high-five and move on.

But that feeling? It lied.

We were flying blind. Every time someone flagged a bad response, we’d tweak something — the prompt, the search config, the embedding model. Then we’d test again and think, “Yeah, this feels better.” Except it wasn’t. We were just playing whack-a-mole.

Fix one issue, another pops up. Improve accuracy for one type of question, and break it for another. No objective signal, no baseline, just an endless loop of subjective tweaks and gut checks. It was frustrating, and honestly, exhausting.

The worst part? We had no idea if we were actually making progress — or just convincing ourselves we were.

Vibe testing gave us false confidence. What we needed was a way to answer two critical questions with real data:

• Is the quality good enough? Does the agent consistently deliver relevant, accurate answers?
• Can we improve it? Are we actually getting better over time — or just spinning in circles?

Vibe testing didn’t give us those answers. It just kept us guessing — and our users feeling the pain.

How Benchmarking Saved Us

Benchmarking was our turning point. After weeks of chasing vibes and duct-taping fixes, we finally admitted it: we needed real signal.

We decided to find an off-the-shelf benchmark to test our assistant against, and we landed on CodeXGlue AdvTest. There was just one small catch: the benchmark wasn’t designed to test end-to-end codebase-related question answering. So, we focused only on the retrieval part of the assistant — which, in our view, was the most impactful piece of the puzzle anyway.

As soon as we ran our first benchmark using BenchX, everything got clearer:

• We finally had a baseline: For the first time, we knew how good (or bad) our assistant really was — and we could measure every improvement from there.
• No more overfitting: We weren’t just fixing the last bug someone noticed. We had a broad, diverse test set to keep us honest.
• We could scale with confidence: Instead of testing with hand-picked questions, we were seeing how the agent held up against hundreds of real-world scenarios.

Leveling Up the Assistant

Our embedding model was decent, but it struggled to consistently rank the most relevant code snippets within the top 10,000 tokens returned. So, we added a search refinement step. Instead of cutting off at 10,000 tokens, we expanded to 100,000 tokens and used Cohere’s reranker model to refine the results.

Cohere’s reranker is a powerful tool that reorders search results by analyzing the relevance of each code snippet to the query, ensuring the best matches rise to the top.

We plugged in the reranking stage, published our next version to BenchX and re-ran the benchmark one more time. The results were stunning:

What If Your Agent Does a Unique Task?

You might be thinking:

“That’s great, but my agent is doing something unique or it depends on a specific database or external API and there is no standard benchmark to test that”

And you’re right. Not every agent can be tested with an off-the-shelf benchmark. But don't give up just yet!

You can still create a custom test dataset and simulate all the interfaces your agent interacts with. But that’s a lot of work, right? You’d have to:

• Create a test dataset that covers all the edge cases
• Mock the databases, APIs, and file systems your agent depends on
• Manage all the infrastructure required to run the tests
• Manage the orchestration of the tests
• Collect and organize the results

At BenchX, we got tired of vibe testing too. So we built a platform that takes all of the above off your shoulders.

If you're serious about agent development, you should be benchmarking. And we can help you get started in 10 minutes.

👉 Book a demo with us so we show you how it works.