{1}
She defines a pattern state on her machine, adjusts geometry and constraints, computes a digest, signs it, and releases the file into the commons where it can be pulled and executed by anyone.
{2}
She deploys a small set of ERC-20 tokens, each mapped to a specific flow in her process-fabrication time, pattern thread participation, and private interaction entry-so each unit can move through contracts as a trigger.
{5}
During fabrication, she groups key production data-pattern digest, material batch, machine profile-into a single reference that the finished garment carries as its trace.
{6}
A buyer sends encrypted measurements through a direct channel; a valid interaction token allows the fitting agent to process that data locally and produce a private pattern instance specific to that body.
She defines a small set of tokens before she definesanything else. Each token is tied to a specific flow inher process. One token corresponds to fabrication time onher machines. Another corresponds to participation in apattern thread. Another corresponds to access to a privateinteraction channel. Each one is an ERC-20 because itneeds to move cleanly across contracts and tools withouttranslation.Her day starts with a pattern state in the commons. Shemodifies it, signs the digest, releases it. That partstays open. The token layer does not touch it. The patternremains a file that anyone can run. The token comes inwhen she wants to sequence what happens around thatpattern.She opens her local workflow engine. It is just coderunning on her machine. A set of functions connected in agraph. Each function expects certain inputs. Some expecttokens. Some expect encrypted data. Some expect both. Thisis where her agents live. Each agent is a function with anarrow role-generate a variation, validate a patternconstraint, compile machine instructions, prepare afabrication job.These agents do not send data outward. They run locally.They read pattern files from the commons and privateinputs from encrypted channels. They produce outputs thateither go back to the commons or stay inside theconfidential layer depending on what she decides.She routes tokens into this graph.A fabrication token enters the system.The workflow checks its presence.The token unlocks a sequence: compile pattern -> generatecut file -> schedule machine -> execute job.The token moves through the graph and is consumed at thepoint where the physical process completes. The output isa garment and a new state anchor. The token has done itswork.At the same time, a buyer token enters another branch.That token allows the buyer to submit encryptedmeasurements. The agent that handles fitting reads theencrypted input, computes a new pattern instance locally,and outputs a private file. That file never leaves theconfidential layer. The token ensures that this step onlyruns for valid participants.She can reward her own agents using the same units.A generative agent produces a set of viable patternvariations. She defines a rule: if an output passescertain constraints and is selected for publication, asmall amount of a design token is routed to that agent'saddress. The agent itself is just a function, but thetoken flow marks its contribution. Over time, she can seewhich agents produce useful outputs by how tokensaccumulate.When she collaborates with another designer, their tokensinteract directly.Her fabrication token can be accepted by anotherworkshop's contract.Their material token can be routed into her workflow toreserve a batch of fabric.No negotiation layer. The tokens carry the interactionforward.All of this runs without merging the open and confidentiallayers.Patterns move freely. They are read by agents, forked,recombined.Buyer data enters encrypted, is processed, and leaves as aresult.Tokens move alongside both, triggering actions but nevercarrying the private data itself.The separation holds because each part of the system onlyhandles what it needs:agents read open files or decrypted inputs inside abounded environment,tokens signal sequence and allocation,encrypted channels carry sensitive data,and outputs are placed either back into the commons orkept local.Her machines sit inside this same graph. They are nodesthat consume instructions and produce material output. Afabrication token reaches the machine node, the machineexecutes, and the node returns a completion state. Thatstate can trigger another token flow-maybe releasing aproof to the buyer, maybe opening a repair channel, maybeupdating a shared ledger of produced pieces.Because everything runs locally or through minimalexternal coordination, no part of her process requiressending raw data to a third party. The agents operate oninputs she already holds. The tokens move throughcontracts she can inspect. The machines executeinstructions she can modify.Over time, her workflow becomes more refined. She adjustshow tokens are issued, how they move, which agents receivethem, and which sequences they unlock. The improvementdoes not come from models "learning" in the background. Itcomes from the structure of the graph becoming moreprecise.Her entire practice becomes a set of executable flows:open pattern states entering from the commons,tokens routing actions across agents and machines,encrypted inputs shaping private outputs,and physical garments emerging at the end of certainpaths.Nothing leaks between layers. Everything moves where it isdefined to move.
{3}
She runs a local workflow graph where each agent is a function: one reads open pattern states, another compiles machine instructions, another evaluates constraints, all executing on her machine without sending data outward.
{4}
A fabrication token enters the workflow and is routed into a sequence that compiles the pattern into cut and weave instructions, schedules her machines, and executes production, with the token consumed at completion.
{7}
The garment is delivered with a proof linked to its reference; the buyer uses that proof to access later interactions-repair, variation, or entry into a private space-without exposing full identity or history.
{8}
Tokens continue to circulate across her workflows and into other designers' systems, routing fabrication, collaboration, and access, while open pattern states expand in the commons and private data remains confined to each interaction.