Workflow and states
Inputs, systems, people, and models involved at each step of the workflow.
The operating model
TokenOps and Runtime aren't two separate methodologies bolted together. They read from the same diagnostic foundation — the same map of a workflow drives what gets funded and what gets executed.
Diagnostic foundation
Inputs, systems, people, and models involved at each step of the workflow.
The decisions being made, the rules governing them, and the approvals required.
Permitted actions and authority; costs, outcomes, and risks; exceptions, escalation, and the evidence each one leaves behind.
Start here
| Result | What outcome this workflow is supposed to produce. |
|---|---|
| Owner | Who is accountable for it. |
| Baseline | Current cost, cycle time, and quality before any change. |
| Budget | What's being spent to run and improve it today. |
| Authority | What an AI system would be permitted to do on its own. |
| Evidence | What has to be recorded to prove it happened correctly. |
| Escalation | What triggers a handoff to a person, and to whom. |
The three-step pattern
States, inputs, systems, decisions, rules, approvals, permitted actions, exceptions, and evidence — usually starting with order exceptions, promotion execution, or close and reconciliation.
Typed schemas, classifiers, guard predicates, action templates, and parameterized queries. Experts review and version every artifact before release.
Validate the case, apply approved artifacts, use models only for bounded interpretation, execute permitted APIs, escalate exceptions, record outcomes.
Entry engagement
Pick one workflow. Baseline its economics with TokenOps. Define the authority and evidence a runtime would need. Leave with a decision-ready plan — fund, redesign, or build — not a slide deck of possibilities.
Map the workflow, its states, and its current cost and cycle time.
Define permitted actions, escalation rules, and the evidence a runtime would record.
Cost baseline, target autonomy, and a build/fund/redesign recommendation.