Every time the AI agent succeeds in completing a step, it learns something new about how to navigate its environment. Each successful test command — every correctly executed click, wait, or verification — becomes a small piece of proven knowledge.
The challenge is to make sure that knowledge isn’t lost.
In human terms, it’s the difference between doing something once and remembering how to do it next time. Without memory, every test would be a reinvention. With it, the agent begins to develop a growing understanding of how to interact with WordPress — one that compounds over time.
This growing collection of experience becomes a library of reusable skills.
Storing Verified Code as Reusable “Skills”
Whenever a step validates successfully, the agent doesn’t just add it to the current test — it also saves it as a simple text file containing the full step code: the action, the wait command, the documentation commands.
These files are stored in a vector database, in my case ChromaDB. Unlike a traditional database that relies on keywords, a vector database understands meaning. It converts each stored step into a mathematical representation of its content — a kind of semantic fingerprint.
This means that even if two steps are described differently, the agent can still recognize that they’re similar. A query for “click publish button,” for example, can retrieve steps that refer to “save changes,” “update page,” or “confirm post.” The agent doesn’t have to know the exact words; it just needs to know what it’s trying to do.
This makes the library incredibly flexible. As the AI grows its collection of steps, it’s not just hoarding data — it’s building a memory system capable of efficient, human-like retrieval.
Searching for and Reusing Existing Steps
When the agent needs to generate a new step, it queries this library. The next intended action — say, “open the plugin settings page” — becomes the search query. ChromaDB then returns the top five most similar results, drawn from everything the agent has already learned.
Each result is a step which worked in a previous test. The agent can examine these and decide whether one can be reused directly or adapted slightly for the current situation.
In practice, this means that the AI rarely starts from scratch. As the library grows, it begins to cover more and more of the common actions: logging in, clicking menus, toggling options, saving changes, checking results.
This reuse significantly reduces the amount of trial and error needed to complete future tests. Once the AI knows how to perform a certain operation reliably, it can reapply that knowledge elsewhere. The time-consuming correction and retry loops from earlier chapters become less frequent — because the agent is no longer guessing. It’s recalling.
The Emergence of Mastery
This accumulation of verified steps marks a turning point. At first, the AI behaves like an eager novice — learning every skill the hard way, step by step, mistake by mistake. But as the library grows, its behavior starts to change.
Now, when faced with a familiar task, it doesn’t experiment blindly. It recalls how it solved a similar problem before, adapts that solution to the new context, and proceeds with confidence. The effect is subtle but profound: the agent starts to develop efficiency.
Each success reinforces the next. In this way, the library becomes not just a collection of files, but a memory of mastery. It’s the record of everything the agent has understood about testing so far, expressed in code that actually works.
It’s also a reminder that intelligence, whether human or artificial, is built not from isolated flashes of insight but from the careful accumulation of reliable, reusable knowledge.