The study sees stage 1 as follows:
Agent Recommender will recommend an Agent Item to a user based on personal needs and preferences. Agent Item engages in a dialogue with the user, subsequently providing information for the user and also acquiring user information.
And as I mentioned, the Agent Recommended can be seen as the agent, and the Agent Items as the actions.
This stage can be seen as a multi-tool agent…
Rec4Agentverse then enables the information exchange between Agent Item and Agent Recommender. For example, Agent Item can transmit the latest preferences of the user back to Agent Recommender. Agent Recommender can give new instructions to Agent Item.
Here is the leap where collaboration is supported amongst Agent Items and the agent recommender orchestrating everything.
There is a market for a no-code to low-code IDE for creating agent tools. Agent tools will be required as the capabilities of the agent expands.
The graphic below from the study shows the Agent Items (which I think of as tools)…
The left portion of the diagram shows three roles in their architecture: user, Agent Recommender, and Agent Item, along with their interconnected relationships.
The right side of the diagram shows that an Agent Recommender can collaborate with Agent Items to affect the information flow of users and offer personalised information services.
What I like about this diagram is that it shows the user / agent recommender layer, the information exchange layer and the information carrier layer, or integration.
