Design Mindset
This chapter describes how to think about building solutions with Operaide — the mental model that ties the platform's architectural choices together.
Operaide is built on one core conviction: a reliable LLM solution is built the way an experienced human would perform the task. Small, well-defined steps. A clear role per step. Clear boundaries, clear error paths, clear escape hatches when a step cannot make a safe decision. This is the opposite of handing an open agent loop free rein over a company's data, tools, and outputs. An autonomous agent is good at improvising; an experienced employee following a well-designed process is good at getting the same question right every time.
This mindset chapter is a placeholder. The full version will address two audiences:
- For people evaluating or adopting Operaide: which kinds of problems fit this approach well, which don't, and how to tell the difference before you commit to a workflow. The goal is to help readers judge their own use cases honestly instead of forcing every problem into a generic RAG or agent pattern.
- For developers building Reaktors: how to decompose a task into aktors, how to write prompts so that each step knows where it is in the process, what happens when it makes a mistake, and what to do when it does not know. How to use LLMs as a guide during development without letting them design the workflow for you.
Until this chapter is written, the clearest concrete expression of this mindset is the Reaktor and Aktor framework itself — a workflow is code, each step has explicit inputs and outputs, and the platform's job is to execute the structure the human designer committed to. Other parts of the handbook that reflect the same thinking are the Aktor framework, LLM calls, and the key concepts in Part I more broadly.
This chapter is being written. The framing above is intentionally short so that it can be linked to from other chapters without committing to content that may shift as the full version takes shape.