OctumAI’s cognitive engine architecture. Bounded cognitive tasks are served by components scoped to those tasks, rather than by a single general-purpose model invoked uniformly for all work. For more details, please visit this page.
Definition
General Agent Replacement (GAR)
GAR is OctumAI’s cognitive engine architecture. Bounded cognitive tasks are served by components scoped to those tasks, rather than by a single general-purpose model invoked uniformly for all work. The architecture is characterized by four properties, each of them necessary: an implementation lacking any one of them is not GAR.
Task-scoped specialization. Work is decomposed into bounded cognitive tasks, and each is served by a component sized to that task rather than by a single model sized to the hardest task in the system.
Operational improvement. The system improves as a consequence of ordinary operation, rather than only through discrete retraining cycles. Improvement is per-task and monotonic: a task that has improved does not regress.
Self-contained inference. Every model involved in producing a response, and in improving the system’s ability to produce it, executes within the operator’s infrastructure boundary. No component of the cycle requires transmitting input to a third-party inference provider.
Engine substitutability. The serving engine is a configuration property rather than an architectural commitment, permitting direct substitution and like-for-like comparison against alternative engines on identical workloads.
These are necessary conditions, not a sufficient definition. A system exhibiting all four is not thereby GAR: the properties describe what the architecture must do, not how it does it, and the how is what separates one implementation from another. That distinction is the same one this program insists on elsewhere — establishing that a component is necessary for a capability does not establish that a set of such components suffices to produce one (§3).
