Code testing using AI-generated users
Launching a new application or updating a critical cloud platform always generates some tension in organizations. The big question on the minds of technical and business teams is always the same: will our system hold up when thousands of real customers start using it at the same time? Until recently, the only way to try to answer this was through cold, conventional load tests that mechanically repeated the same click over and over again. However, real human behavior is chaotic and unpredictable. Therefore, the technological ecosystem has evolved towards a much more realistic and effective approach: code testing using users created with Artificial Intelligence (AI).
How and why is this done?
Traditionally, to check the resilience of a computer system, software quality assurance (QA) engineers programmed static scripts that simulated an ideal navigation path (the famous happy path). The problem is that this manual scenario design is complex, inefficient, and does not reflect reality. Real users hesitate, go back, open several tabs, make mistakes when filling out a form, or abandon the shopping cart unexpectedly.
To solve this, advanced generative capabilities like those of Vertex AI and Gemini are used today. Why? Because instead of programming a rigid line of code for each action, AI allows us to auto-generate dynamic intelligent agents that act as “virtual humans” or synthetic users.
How is it done? The system analyzes the structure of your application and historical navigation patterns. With this information, the AI creates varied and highly realistic behavioral profiles. These virtual agents navigate the platform executing unpredictable routes, interacting with payment gateways, registrations, and queries, subjecting the elastic infrastructure to a transactional stress identical to what will be experienced on the official launch day.
The great advantages of testing with “virtual humans”
Adopting this predictive and intelligent approach provides drastic competitive advantages over the methods of the past:
- Ultra-realistic simulation: By replicating the hesitations, errors, and complete transactional flows of the human user, the system is stressed under true business conditions, not artificial ones.
- Detection of silent failures: It allows identifying hidden problems such as memory leaks, subtle database locks, or crashes in business logic before they affect a single real customer.
- Massive time and cost savings: Designing these scenarios manually required weeks of engineering work; with AI, the effort is drastically reduced, accelerating the time-to-market.
- Cloud infrastructure optimization: It allows you to precisely know the true physical limit of your computer systems, avoiding server over-provisioning and reducing unnecessary computing costs.
What happens next? How test success is evaluated
Once we launch our “army” of AI users to interact massively with the system, the crucial phase arrives: evaluation. It is not enough to know if the website crashed or stayed up; we need a surgical diagnosis.
For this evaluation to be predictive and accurate, the process relies on an architecture of advanced monitoring and observability. Market-leading tools like Prometheus and Grafana are in charge of collecting and visualizing real-time performance metrics (latencies, CPU usage, memory consumption, or error rates per second).
In addition, log centralization systems like Loki analyze internal code logs to track the exact root cause of any slowdown. In this way, upon concluding the test, teams obtain a detailed Quality Score and interactive dashboards that show precisely which lines of code or which cloud configurations need to be optimized before going to production.
The complexity of code testing setup
Although the benefits are amazing, implementing this technology from scratch is not a simple task. Setting up an environment capable of simultaneously emulating thousands of users created with AI involves high technical complexity.
At the infrastructure level, it requires spinning up and orchestrating container clusters (like Kubernetes) so that virtual agents can run in an isolated and scalable manner. At the communication level, it is essential to deploy robust messaging layers based on queue systems like Kafka to process massive event flows without saturating core operating systems. As if that weren’t enough, the entire environment must be secured using API Management tools and applying advanced prompt engineering techniques so that AI models act with total precision and without deviating from corporate security margins.
Trying to coordinate all these pieces manually can consume months of development and divert your teams from their true business focus.
How to update your development cycle with AI?
To eliminate this friction and drastically simplify the process, at Luce IT we have created reCode AI, our asset to inject Artificial Intelligence into each phase of the software development lifecycle. Its purpose is to make the construction, maintenance, and validation of technological platforms agile, clean, and free from burdensome manual tasks.
Within the reCode AI cycle, the critical testing phase is automated and simplified through the specialized Code2Test module.
If you want to discover all its possibilities, get to know reCode AI.
Frequently asked questions about development testing with AI
What is the difference between a traditional load test and one performed with AI-created users?
Traditional tests use rigid and linear scripts that mechanically repeat the same action. In contrast, users created with AI act as dynamic “virtual humans”: they hesitate, make mistakes, change routes, and alter transactional flows in real-time. This allows replicating the chaotic behavior of customers with total fidelity in a real production environment.
How does Artificial Intelligence help reduce Cloud infrastructure costs during tests?
By simulating ultra-realistic bursts of massive traffic, AI helps predictively and accurately identify the physical limits of the software and the cloud’s elastic scaling rules. This prevents technical teams from having to over-provision servers “just in case,” translating into a precise adjustment of computing resources and direct economic savings.
What role do monitoring tools play after running AI tests?
They are the key piece to evaluate the success of the tests. While AI users stress the system, advanced observability tools capture and process millions of interactions, performance metrics (like latency or CPU usage), and code records (logs). All this information is translated into a surgical diagnosis (Quality Score) that indicates exactly which lines of code must be optimized.
Is it very complex to integrate these types of tests into an existing development cycle?
Yes, setting up an infrastructure from scratch that coordinates container clusters, massive message queue systems, and security layers for APIs involves high technical complexity. However, integrated initiatives like reCode AI and its Code2Test module completely absorb that architectural burden, automating the tests so that your teams do not lose valuable development time.
How are data security and privacy guaranteed when using intelligent agents in tests?
At Luce IT, we apply strict Security by Design methodologies. Virtual users are configured through advanced prompt engineering and security guardrails that rigorously control the inputs and outputs of corporate AI. This guarantees that simulations are executed in a controlled, precise manner and under the organization’s full regulatory compliance, without the risk of information leaks.
