Learning (education, MBA, graduation)
When a new employee starts at a company, he brings with him a set of knowledge and skills that he acquired throughout his education and previous experiences.
But that doesn’t mean his learning is complete.
In fact, it’s common for companies to invest in training and development so their employees learn new skills and stay up to date.
This can range from internal workshops and seminars to funding external courses and graduate degrees.
With AI Agents, the principle is similar.
When we create an AI Agent, it already comes with a base of knowledge and skills that were “learned” during its initial training (the process of creating the language model that serves as its “brain”).
But like with humans, this initial learning is not always sufficient.
For our AI Agents to continue evolving and adapting to the specific needs of our business, it can be beneficial to invest in their continuous learning.
There are several advanced techniques to do this, such as fine-tuning, reinforcement learning, and continuous learning.
But we won’t delve into the technical complexity of these approaches here.
Instead, let’s focus on one specific technique that is particularly powerful and accessible: memory expansion.
Think of the following analogy: imagine you have a highly qualified and competent employee, but who every now and then needs to learn some new information about the company’s products, services, or processes.
One option would be to send that employee back to school or intensive training so he could relearn everything from scratch.
But a much more efficient approach would be to simply provide him with the relevant materials, like an updated manual, an instruction guide, or a notebook with the new information.
With AI Agents, we can adopt a similar strategy through memory expansion.
Since we generally don’t have direct access to the Agent’s “brain” (language model) to make complex modifications, the most effective way to “train” it with new information is by providing external materials for it to “read” and absorb.
This can include things like knowledge bases, internal documents, customer service records, or any other type of data that’s relevant to the tasks and functions of that specific Agent.
The big advantage is that AI Agents can “read” and process these materials very quickly, allowing them to update and adapt much more agilely than would be possible with a human employee.
Furthermore, this approach tends to be much more cost-effective than trying to retrain the Agent’s language model from scratch every time there’s new information to be learned.
In the next topic, we’ll explore in more detail how this memory expansion works and how we can use it strategically to keep our AI Agents always up to date and aligned with the needs of our business.
But for now, the key point is to understand that, although continuous learning is not always strictly necessary for AI Agents, it can be extremely beneficial in many cases.
And that one of the most powerful and accessible ways to achieve it is through smart management of the “external memory” of these Agents.