How AI Agents Work: From Prompt to Reply

If you’ve ever interacted with a standard chatbot, you know they are great at answering questions but rarely “do” anything on their own. In the tech world, we are undergoing a major transition: we are moving from the era of AI that just talks to the era of AI Agents.

But what does this change in the routine of those who develop or test software? Basically, everything. Understand the logic behind it in today’s blog.

What Defines an AI Agent?

The main difference between standard generative AI and an agent is autonomy. While a typical large language model waits for a command to generate text, an agent receives an end goal and decides, on its own, which steps it must take to achieve it.

If you ask a chatbot to “test the login,” it might explain how to do it. An AI agent, on the other hand, opens the browser, identifies the fields, enters the data, clicks the button, and verifies if the system behaved as expected. According to McKinsey’s The State of AI in 2023 report, companies adopting AI for automation and product roles are already reporting operational efficiency gains due to these tools’ execution capabilities.

Understanding the backgroud of an AI Agent

To understand how this engine works, we need to open the “black box.” An AI agent is a software architecture that operates on an iterative cycle of four fundamental pillars.

1. Perception and memory

The agent begins by interpreting your command (the prompt) and analyzing the environment. To be efficient, it needs short-term and long-term memory. Short-term memory stores what just happened in the previous test step, while long-term memory retains project context and business rules. This prevents the AI from “forgetting” that it already filled in the email when trying to click the submit button.

2. Planning (chain of thought)

Upon receiving a command like “Validate the checkout flow,” the agent uses a technique called Chain of Thought. It doesn’t execute everything at once; instead, it creates a logical action plan:

• Decomposition: It breaks the goal into smaller tasks (add to cart, fill in address, validate payment).
• Self-reflection: The agent reviews its own plan before starting, correcting inconsistencies that could lead to errors.

3. Tools

An AI agent cannot “click” anything on its own; it needs hands. In agent architecture, we call this Tool Use. The agent has access to a set of functions, such as APIs and browser selectors. When the “brain” decides on the next step, it calls the specific tool to interact with the code or the interface.

4. Feedback loop and self-correction

This is the great differentiator. If the initial plan fails, for example, an unexpected pop-up blocks the screen, the agent receives this error as new input. It analyzes the obstacle, reformulates the plan, and tries a new approach in real-time. The State of AI 2023 study by Air Street Capital points out that this “closed-loop” reasoning capability is what allows for a drastic reduction in manual system maintenance.

Why AI Agents are the future of QA?

The traditional QA model is often slow and dependent on rigid scripts. Capgemini’s World Quality Report 2023-24 highlights that the lack of intelligent automation remains one of the biggest bottlenecks for rapid software delivery.

Agents end the era of “brittle tests.” Instead of a script failing because a button ID changed, the agent understands the context and continues execution. This democratizes the process, allowing managers and product professionals to describe what needs to work in natural language, without relying exclusively on technical code.

Meet TestBooster.ai: your quality agent

This is where theory transforms into core infrastructure. TestBooster.ai acts as a quality hub that uses AI agents to organize and execute your entire company’s testing strategy.

Within the platform, you describe your flows in natural language and our AI translates that intent into automated scenarios. If you define the goal as “the account opening process must always work,” the agent will act adaptively. Even if the layout changes, the test remains resilient.

Additionally, TestBooster.ai centralizes legacy automations (such as Selenium and Cypress) into a single dashboard. This eliminates silos between teams and gives managers a real view of business quality, transforming technical failures into strategic insights.

Want to see how our AI agent can take over your test execution today? Talk to our team.