Oh, this one’s interesting! One approach we found really effective is to use multiple LLM judges instead of relying on just one. Think of it like a jury system, you get a bunch of opinions rather than leaving everything up to a single judge who might have their own biases. Then, we do some spot-checking with humans to make sure the system isn’t drifting off-track. It’s a simple but practical way to keep the evaluation fair and balanced
Honestly, in our team, we keep it pretty straightforward. Whenever the AI suggests test cases in the IDE, we don’t just blindly accept them. Instead, we run a coverage check to see if they’re actually filling a gap in our tests. If a suggestion adds real value, it stays; if it’s just repeating what we already have, we toss it. It’s simple, but it works, and it keeps our tests meaningful instead of turning into a bunch of boilerplate.
Absolutely, AI agents can sometimes be biased when testing enterprise AI models. From what I’ve seen, they tend to favor outputs that look “polished” or confident, even if they’re not actually correct. It’s a bit like giving extra points for style over substance.
The good news is, there are ways to keep this in check. Running bias audits and having domain experts review the results can help catch these blind spots and make sure the evaluations are more accurate and fair. Essentially, it’s about combining the speed of AI with human judgment to get the best of both worlds.
The key is finding the right balance. What worked well for us was splitting the approach, daily, we run quick automated evaluations to flag issues early and keep development smooth. Then, on a quarterly basis, we dive deeper with audits that combine both human review and AI checks. This way, we cover both speed and depth without stretching the budget too thin.
When it comes to testing agentic AI for enterprises versus SMBs, the priorities are a bit different.
For large enterprises, the focus is usually on things like auditability, compliance, and being able to scale reliably. Big organizations need to make sure the AI behaves in a way that’s transparent, traceable, and meets strict regulatory standards. Testing here goes deep into making sure every decision can be explained and that the system can handle large workloads without breaking.
On the other hand, SMBs care more about getting started quickly and integrating smoothly with their existing tools. They don’t have the same level of red tape, so the testing approach leans toward making sure the setup is simple, the AI plugs in easily, and results show up fast.
So, the core difference is: enterprises test for control and scale, while SMBs test for speed and usability.