In performance testing, AI’s confidence can be your team’s undoing

Quick summary: AI accelerates code creation, but its inherent confidence pushes structural risks downstream, where they surface as costly, release-blocking problems. As code output scales, performance validation that can’t keep pace becomes a headache and a business risk. Agentic performance testing embeds skepticism and performance awareness into the development process before risk can compound.

Software development requires specialized expertise for a reason. When you ask a developer whether their code works and they confidently say “yes,” it’s because they wrote it. They know how it works, and they’re accountable for its functionality.

But because AI can reduce the barriers of some of that work, its adoption in software engineering is no longer optional. If done correctly, companies that inject AI into some of their most critical workflows can enjoy cost savings and business acceleration. And if you ask AI for a performance review of itself, it’s the MVP every time.

Why shouldn’t it give itself high marks? AI bears none of the risk if a live feature crashes under peak load, or a memory leak brings the system down.

Free-rein AI doesn’t flag guesses or tell you when it’s unsure. Even when an approach is suboptimal or context‑blind, the output arrives wrapped in clean abstractions and confident naming. Because humans equate confidence with correctness, we can easily accept these choices, especially when pressure is mounting to work faster than we ever have before.

But if your organization is still putting performance validation at the end of the SDLC, that overconfidence will present a big problem right before your go-live date, when performance tests reveal an issue that requires weeks of rework.

What organizations have in AI-generated code is both a potential risk and a big opportunity. If implemented correctly, agents writing code can be a huge accelerator, but if you leave fundamental pieces of the process like performance testing at the end – the way most enterprises currently do – you’ll end up with massive amounts of code hitting the same bottleneck you’ve always hit.

More: Tricentis NeoLoad for end-to-end performance testing

Confidently generated code, tested too late

When a large language model (LLM) gives you a response that seems too confident, what you’re really seeing is an absence of doubt signals. An agent is unlikely to question your idea or assess risk before writing code; it just writes the code you asked it to.

Especially after it has done the job correctly a few times, AI’s confidence in its own output gives human reviewers confidence that it’ll do it correctly most of the time.

This matters because most organizations still rely on human intuition and downstream testing to validate nonfunctional behavior. Software development teams check whether code runs and meets requirements, but performance, scalability, and resource usage in real-world conditions are validated later, by a different team in a different environment.

Early design decisions that shape performance behavior like how state is managed, where work is serialized, or how resources are allocated rarely trigger functional failures. By the time performance testing begins, those decisions have been layered on and reused across the codebase, so if the performance validation process identifies big structural issues right before release, you have the familiar bottleneck.

When you’re trying to move fast with a scaled software development process, the bottleneck introduces more risk and requires more time-consuming and costly fixes.

Webinar: Engineering the future of shift-left performance testing

This is a problem you can imagine at a startup: You’re moving extremely fast, standing up features, and releasing code all the time, and testing is more of a gut check than an actual gauge of the software’s quality. As a result, the issues flagged during performance validation raise the question “can we get away with this” rather than “how do we fix this.” The technical debt left behind is tomorrow’s problem.

As larger enterprises adopt AI coding tools, they risk falling into a similar pattern wherein speed outpaces governance.

In a recent example, a portion of the code powering a leading AI company’s LLM was leaked and revealed that a compaction bug was burning 250,000 API calls per day. It’s unclear how long that was in production before it was noticed, or how long after it was noticed it got fixed, but one estimate puts the error’s cost at $750 per day in wasted compute.

These are preventable problems before agents are writing the majority of your organization’s code. Once they are, the financial, technical, and reputational risks are amplified.

Agentic testing closes the gap

If AI accelerates the speed at which code enters a system, validation must move as quickly. The goal of agentic performance testing is to introduce performance awareness the moment substantial changes are made to the codebase. If the agents aren’t going to second-guess their own code, someone else needs to.

In this model, a context‑aware agent continuously reviews the changes and how they fit into the wider system. Imagine an agent that’s able to validate from design through analysis and seamlessly produce performance reports in minutes and then continue to review code that’s checked in on a daily or weekly basis, depending on what the team needs.

Webinar: See agentic performance validation in action

This is what “shifting left” looks like in the era of AI-generated code. Instead of a bottleneck to the release process at the end, performance agents are embedded directly into the software delivery process, constantly reviewing and analyzing results. Risks are flagged early while the change is still small, and while alternative approaches are still cheap.

Ultimately, AI’s overconfidence is just exposing and exacerbating the problem with the process many teams are using to validate. Now, instead of one big project hitting one bottleneck once, we’re living in a world where features can be built rapidly and shipped to production weekly or daily.

We’re already done building software like we used to, so why would we let the old way of performance validation persist?