LESS SEARCHING. CLEARER NEXT STEPS.
Notes and review marks here are local simulations. They never place an order, receive stock or confirm a shipment.
DESIGN → TRY → CHECK → REVISE
A bounded experiment in how purchasing, warehouse and management find their next action.
Purchasing: identify a buy candidate, recognize a cost hold and follow up an existing PO. Warehouse: distinguish needed from packable units, respect prerequisites and leave a durable handoff. Management: find a blocked decision and the evidence needed to move it forward.
James, Dalton, Zander, Isaac and Robert name the intended roles. Generated feedback is not their actual feedback.
V1 is this experiment’s flat queue. V2 groups the same rows by next action and puts decision evidence beside quantity. Neither is a pixel-for-pixel copy of the live dashboard. Compare both with fresh sessions and the same scenario.
Score correct decision, evidence found, missed blockers and handoff persistence. Treat agent time as a diagnostic, not human time saved.
Zero critical mistakes is the first gate: never treat missing evidence as clear, pipeline stock as free stock, a local check as a shipment, or a suggested quantity as authorization.
Use unseen product variants and an independent reviewer. Once a test influences the design, it is a regression test, no longer a holdout.
Three short rounds are the proposed first study. Change only a few things per round; keep the earlier variant. Stop for critical regressions or two rounds without useful gains. Before promotion, observe real task attempts from purchasing, warehouse and Robert.
No human trials have been run for this prototype. No automatic production promotion or scheduled iteration is configured.
Google reports recovering 0.7% of worldwide compute resources through an evolved scheduling heuristic. The transferable practice is measurable evaluation with correctness checks—not an assumption that algorithm gains predict UX gains.
Read Google DeepMind’s report ↗Anthropic describes Descript’s human-calibrated grading and separate quality/regression suites, and Bolt’s combination of static analysis, browser agents and model judges. These are practitioner reports, not proof that synthetic users replace employees.
Read the evaluation case studies ↗A retail-agent study found success rates varied by up to nine percentage points across simulated-user models, with different failures from real people. This is evidence for human calibration; its conversational setting differs from this dashboard.
Read the research paper ↗Agents can share the same blind spots and agree because of their model or prompt. Give reviewers tasks rather than designer explanations, vary scenarios, and compare predictions against real team behavior.
Fewer clicks can hide a warning. Never trade correct quantities, visible uncertainty or role-appropriate information for speed. Score critical failures separately; do not bury them in an average.
Copied pages can retain live API URLs, shared browser state and privileged data. This prototype instead has a separate origin, fictional fixtures, no production bindings and a policy that blocks network data requests.
Use stale and missing evidence, save failures, interrupted notes, shared components and mobile tasks. The current small fixture cannot establish behavior at production scale or cover all dashboard workspaces.
Source inspection identified mixed purchasing actions, ambiguous pipeline stock, fragile handoff drafts and production API coupling. The standalone prototype addresses these as hypotheses. First browser trials found contradictory packing instructions under stale/missing evidence. The revised prototype replaces the primary instruction with verification, labels historical values, and adds a shared purchasing-to-warehouse PO handoff. Browser verification and independent review receipts are recorded with the task branch; they do not establish human productivity gains.
V1 and V2 share the same synthetic business facts. Only the prototype is published; existing dashboard files, workers, bindings and deployment are outside this change.
What were you trying to do, what happened, and what did you expect? This stays on your device until you download and share it.