1. A system, running on one or more processors, for client-side implementation of an interface automation language at runtime, comprising:

agent specification logic, running on client-side, and configured to construct an agent specification, and to make the agent specification available for server-side translation into an intermediate representation, wherein the agent specification is configured to automate a multimodal interface workflow; and

     • runtime interpretation logic, running on the client-side, and configured to:
     • receive the intermediate representation;
     • detect one or more agent functions in the intermediate representation;
     • generate one or more agent calls based on the agent functions;
     • issue the agent calls to an agent, and, in response, receive at least one runtime                       actuation function from the agent; and

translate the runtime actuation function into at least one runtime actuation command, wherein the runtime actuation command triggers at least one machine-actuated action as a runtime synthetic action that automates the multimodal interface workflow.

1. A system for magnitude-invariant image-text agentic interface automation, comprising:
memory storing an input image and an input text sequence;

patch extraction logic configured to extract image patches from the input image on a line-by-line basis, and generate a plurality of lines of image patches for the input image;

bit vectorization logic configured to convert image patches in the plurality of lines of image patches into magnitude-invariant bit vectors, and generate a plurality of lines of magnitude-invariant bit vectors;

newline insertion logic configured to interleave a newline character between successive lines of magnitude-invariant bit vectors in the plurality of lines of magnitude-invariant bit vectors, wherein the newline character specifies an end of a line in the input image;

tokenization logic configured to translate the input text sequence into a sequence of input text tokens, and to translate the successive lines of magnitude-invariant bit vectors interleaved with the newline character into a sequence of input magnitude-invariant bit vector tokens;

linear projection logic configured to linearly project a single token stream of the sequence of input text tokens and the sequence of input magnitude-invariant bit vector tokens into a decoder-only Transformer logic, wherein the linear projection of the single token stream bypasses any embedding lookup; and

the decoder-only Transformer logic configured to process the linearly projected, embedding lookup-bypassed single token stream to generate a sequence of output tokens that are responsive to the input image and the input text sequence.

1. A system for generating training data to train agents to automate multimodal interface task workflows, comprising:

• an intermediary interposed between an interface, comprising multimodal content, and a user, and the intermediary is configured to:
  intercept one or more user-actuated actions directed towards the interface by the user, wherein the user-actuated actions, if received by the interface, execute a task on the interface;
  preserve a state of the interface prior to the execution of the task, wherein the preserved state includes multimodal data comprising arbitrary-length text sequences and arbitrary-resolution images;
  translate the user-actuated actions into one or more actuation commands using a transformer-based multimodal neural network comprising multi-head attention mechanisms, wherein the actuation commands are configured to trigger one or more machine-actuated actions that replicate the user-actuated actions on the interface to cause automation of the multimodal task workflow; and generate a multimodal training dataset to train an agent to automate the task, wherein the training dataset requires the agent to process, as input, the multimodal state of the interface prior to the execution of the task, and to generate, as output, the actuation commands, wherein the translation and generation are performed using runtime interpretation logic dynamically executing on a client-side computing device.