a small desktop entity occupies a tiny window. you offer it whispered fragments of text – a line from a song, a passing thought, a brief moment from your day. a generative model processes these inputs and slowly cultivates an abstract visual bloom, unique to your entries. the textures and colors draw from the quiet palette of an old library or the focused calm of a coding environment. it becomes a tiny, ever-evolving piece of digital art reflecting your inner weather, a subtle presence for those quiet hours spent missing somewhere else while building something new.

perceptor module:
raw feed hits this first.
it rapidly extracts a lean feature vector, aiming for disentangled signals.
lightweight parsers or a tiny specialized vision model for ui elements if visual.

state tracker:
holds the agent's current world view, super compact.
updates based on perceptor's output and predicted action outcomes.
processes quick diffs.

deliberator/planner:
takes current state and active goals.
selects the next action.
associative memory helps recall similar past successes.
small, fast policy network, possibly with equivariant properties for generalization.

action executor:
translates the policy's chosen action into actual commands.
browser clicks, api calls, whatever the system needs.

evaluator/learner:
watches the action's outcome.
provides feedback: did it work, any errors, what changed.
feeds back to update the policy, the state tracker's predictions, and logs failures.

communication between these modules needs to be blazing.
direct calls within a tight process or minimal message queue.
main cycle is perceive -> update state -> deliberate -> execute -> evaluate.