The folder structure
The default Nolita project uses the following folders to delineate each layer of an agentic application:
/agent
includes the creation of a chat completion API, which is then passed to the Nolita Agent class for integration with the browser state machine./app
includes all front-end code for the application./extensions
includes the integration of inventories as well as defining custom types for your responses from the agent./server
includes all back-end code for running the browse loop.
Inspiration
While working with other agentic companies, our research found the following separation of roles, which inspired the structure of the Nolita project.
LLM
- What model is used on the 'bottom layer' of the stack?
- Do you allow users to swap out underlying models?
- System prompt is included here.
Agentic logic
- What is the agent’s prompt on top of the underlying system prompt?
- What’s the structure of the event loop we place an LLM in?
- Does it self-iterate and improve? Is it set?
- Is it a formal state machine, or is it entirely prompt driven?
Toolchain
- How do we define what actions the LLM can perform?
- Is the toolchain defined as part of prompt manipulation, or is it formally constrained (as in, proceeding along a graph of possible actions)?
- By going entirely prompt-driven, one can fall into “phantom actions” (reporting an action is undertaken, as opposed to making it such that saying an action is the same thing as the action itself.)
- Let me expound a little here: there’s a situation where by simply saying “I’ll invite this person” is itself part of a command to the toolchain, as opposed to a separate report to an observer. When writing entirely prompt-driven applications, one can hallucinate the structure of the action.
- Do we write an underlying API to formalise all commands without directly hitting external APIs?
Event loop manipulation
- Does the agent then “double check its work” before proceeding to presentation layer?
- Do we GOTO 1 so to speak, if it performs incorrectly?
Presentation
- How transparent is the stack to the user?
- Is the agent abstracted as a “product” (with an agent’s artificial monologue puppeteering a conventional software stack), or anthropomorphized in its own right?
- Is the user configuring tasks, agents, events?