Imagine your bank hires an experienced analyst to help onboard new clients.
This person knows banking inside out. They have read every article, every book, and every regulation on banking.
And the reading doesn’t stop at banking.
They have also patiently studied an enormous amount of publicly available information.
On paper, this is the perfect hire.
Then the first day comes.
The first client application lands on their desk. It’s from James.
They know how banks onboard clients. But they just don’t know where to start with this application.
So what went wrong?
Buying an AI model works the same way.
An AI model has learned from an enormous amount of publicly available information. Ask it almost anything about banking, and it will answer. But it still cannot do useful work for your bank, just like your brilliant analyst couldn’t.
Because it has never seen a single customer file, read your internal documents, or learned how your bank actually works.
So what’s the first thing you do?
You point the analyst to a shelf. It contains everything they need to understand how your bank works—client onboarding procedures, internal policies, and the regulations the bank follows.
Now the analyst knows exactly where to look when they need one of the bank’s procedures.
But you don’t ask them to read the whole shelf and hold it in their head.
You let them pull down one manual at the moment they need it.
If James mentions in his application that he recently sold his software company and wants to move several million dollars into a new account, that immediately tells the analyst where to look. They pull down the bank’s source-of-funds policy, read that procedure, and continue with the case.
That simple act is exactly how Retrieval-Augmented Generation works.
You don’t expect the model to carry every document in its memory.
Instead, you build a system around it that retrieves the right document when it’s needed. If James mentions a business sale in his application, the system retrieves the bank’s source-of-funds policy, and the model uses that policy to respond.
That is one of the building blocks of an AI agent, and it’s called RAG.
But the shelf only holds the bank’s own rules.
It says nothing about James himself.
Now the analyst needs information that isn’t written in any policy. They need James’s identity documents to confirm he is who he claims to be. They need to check his name against sanctions lists and politically exposed person registries.
Some of what the analyst needs lives inside the bank. Some of it comes from outside. Either way, they can’t reach any of it on their own.
So the bank gives the analyst tools to access those systems. One tool opens the customer records. Another searches the sanctions database. A third contacts an external identity verification provider.
With those tools, the analyst can pull up James’s file, check whether his name appears on a sanctions list, and confirm he is who he says he is.
The AI model works the same way.
On its own, it can only reason from what it already knows. A tool grants it access to one of your bank’s systems, allowing it to retrieve James’s real file or check the latest sanctions list, rather than relying on memory alone.
But looking things up is only half the job.
The analyst also has to act on what they find. They might open an onboarding case, record their findings, request another document from James, or flag his application for a human to review.
A model, on its own, can’t do any of that.
The moment you give it tools that let it retrieve information and take action, it becomes an AI agent.
That is the second building block of an AI agent: tools.
Your analyst can finally do real work.
But hand them James’s application again tomorrow, and they have forgotten everything about it.
They still understand banking. What they no longer remember is James, the client they worked on just yesterday.
An AI model forgets in the same way.
It keeps all the general knowledge it came with. What it doesn’t keep is anything it picked up while working on James’s case.
So why does it seem to remember?
Because each time the model is asked to continue with James’s application, it is handed everything that happened earlier in the case. The model isn’t recalling any of it. It is simply reading through it again before producing its next answer.
That is what we call short-term memory.
But onboarding James rarely happens in one sitting.
One day the bank verifies James’s identity. The next day, it reviews his source of funds. These two checks happen on separate days. Without memory, the model starts fresh each time and forgets everything from the day before.
So you give the model its own filing cabinet, just as you would for the analyst.
When the day’s work is done, the model writes the important details into that cabinet. The next time James’s application comes in, it opens the same folder and picks up right where it left off.
That stored record is long-term memory.
This is the third building block of an AI agent. It’s called memory.
Your analyst now has everything they need. They know your bank’s procedures, they can reach its systems, act inside them, and carry a case forward from one day to the next. So you hand them James’s application.
But James isn’t a simple client. His identity has to be verified. His name has to be screened against sanctions lists. The sale of his software company has to be validated. You could ask one analyst to perform all of those checks, one after another.
Or you could build a team.
One analyst verifies identity. Another handles sanctions screening. A third reviews the source of funds. A fourth validates the business sale.
The best part is that none of those checks depend on the others. Instead of waiting in line, they all run at once, reducing work that once took days to minutes.
A team, though, needs a manager.
Someone has to read James’s application, decide which analysts are needed, give each one the right information, and bring all the findings back together.
In an AI agent, that manager is called the orchestration layer.
It decides which specialists run and gives each one exactly what it needs. Their findings then go somewhere else to be brought together, which is a separate job with its own rules.
That is the fourth building block of an AI agent. It’s called orchestration.
Now the picture is complete.
On its own, the model is the analyst on their first day, brilliant and unable to help you.
Give it your bank’s documents through RAG, and it finally knows how your bank works. Add tools, and it can reach your systems and act inside them. Add memory, and it carries a case from one day to the next.
Put an orchestrator above a team of these analysts, and the work that once crawled through one desk now moves as a coordinated team.
Put those building blocks together, and the brilliant analyst on day one becomes someone who can actually onboard James.
That’s an AI agent.
Building one is the easy part. Proving it works well enough to put in front of a compliance officer is a different problem, and that’s what the rest of this series is about.