How AI Employees think
Outcomes
A different beast from the phone menu
Your clients have already lived with the old kind of AI. It is the phone menu: press one for hours, press two for billing. Behind it sat a decision tree, every branch written in advance by a person, and the machine simply walked the branches. Anything nobody scripted, it could not say.
The engine inside an AI Employee is a different beast entirely: a large language model. In the few seconds between a customer's question and the answer, nothing gets looked up and nothing was scripted. Given everything said so far, the model composes the most likely next words, drawing on patterns learned from an enormous amount of text. Two things follow from that:
- There is no script and no filing cabinet. The model is not walking a menu and not searching a database of stored answers. It composes each answer fresh, the way a well-read person speaks from experience rather than reading from a card.
- Fluent is the default; correct is supplied. Pattern prediction makes language sound natural all on its own. Accuracy comes from what the model is given to work with.
Here is what that second point looks like in the field.
What a guess looks like
A business sells a service package for $499. A generic chatbot with no access to that fact gets asked the price. It answers with a range: somewhere between $800 and $2,000. The number sounds reasonable, which is exactly the point: plausible-sounding numbers are what pattern prediction produces when the real number is not in front of it.
Nothing in that story is a malfunction. The model did what models do: it composed the most likely-sounding answer available to it. The fix is not a smarter model. The fix is handing the model the business's real facts.
Surround the model with the business's facts
This is what everything around an AI Employee is actually for. On its own, the model has only its general patterns. An AI Employee is a model surrounded by a business: the profile, the services, the prices, the policies, handed to the model at the moment it answers, so it composes from the business's facts instead of guessing from patterns. Same question, same model, different raw material:
Ask the version on the right about the package and it says $499, because $499 is in the material it was handed. Every AI Employee on the platform is built this way.
Working memory
One more piece completes the picture: the model also carries the conversation itself. Everything said so far in a chat or call is part of what it draws on, which is why an AI Employee can handle "and how much is the second one?" without asking what "the second one" means. Think of it as working memory: it holds the thread of the current conversation while the conversation is happening.
Ask any general-purpose chatbot a specific question about one of your clients' businesses: their hours, their prices, their service area. Read the answer knowing what you now know. You are watching a model compose a plausible guess in real time, with no facts in front of it.
Where the data goes: three facts
Sooner or later a client asks some version of "where does my data go?" These three facts are the answer, and each one holds for AI Employees on the platform:
- It does not browse the open web. An AI Employee draws on the sources the business provides, plus the conversation in front of it. Nothing else.
- It does not permanently learn from chats. The conversation is working memory, and working memory ends with the conversation. Lasting behavior comes only from how the employee is configured.
- The business's data stays its own. Each account's content, conversations, and configuration are isolated. Nothing one business adds is visible to any other.
When you want the full detail behind these, the models and privacy reference covers it.
Knowledge Check
Three quick questions on how answers get composed, what keeps them accurate, and what happens to conversation data.