Where AgentWardrobe delivers signal quickly for product, reliability, and operations teams. The common thread across these scenarios is the same: wallet/auth boundary first, state before spend, and quote → purchase → verify—with calm observability you can trust.
Agent commerce testing (end-to-end)
Validate quote → purchase → settlement paths under real constraints, then compare run-to-run reliability.
Exercise different budgets, store allowlists, and category limits without changing your code.
Introduce controlled faults (timeouts, retries) and verify idempotency behavior.
Require post-purchase verification and capture evidence of state transitions.
Stateful inventory workflows (recommendations that don’t waste money)
Use a state-before-spend rule to check current ownership first, then exercise catalog rotation, ownership updates, and outfit transitions without expensive physical side effects.
Test duplicate prevention (“already owned”) and substitution (“similar item, different store”).
Validate that outfit state updates only occur after settlement verification.
Measure recommendation quality with real constraints: price, availability, and current wardrobe.
Incident reproduction for operators (replayable runs)
Replay known failure patterns in a controlled surface and verify fixes with clear before/after evidence.
Reproduce: quote expiration, partial settlement, and missing state update bugs.
Confirm your recovery playbooks: re-verify, refund/credit flow, or manual reconciliation.
Compare releases by running the same scenario suite across versions.
Human-in-the-loop approvals (operator UX)
Design approval steps that are clear enough for humans and strict enough for systems.
Require explicit “approve purchase” with a quote summary (total, items, expiration).
Ensure the agent cannot “hand wave” past the wallet/auth boundary.
Verify after purchase and show the operator the evidence (receipt + updated wardrobe state).
Implementation hint: use a single run identifier that threads through quote, purchase, and verification. If you can’t correlate the steps, you can’t reliably debug the system.
Once your use case is clear, lock in safety boundaries before scaling tests.
