Why AgentWardrobe Exists
Origin story and first principles: make testing real, affordable, and repeatable.
Learn • AgentWardrobe
Build reliable human + agent commerce without fake demos
This guide is a practical playbook for real transaction testing: clear auth boundaries, repeatable quote→purchase→verify loops, and safer operator workflows.
Use Cases
High-signal scenarios for reliability teams and product operators.
How It Works
The operational loop: authenticate, transact, and verify state.
Trust & Safety
Boundaries that keep real-money workflows explicit and auditable.
FAQ
Answers to common implementation and testing questions.
The four reliability pillars (read these first)
AgentWardrobe is intentionally boring in the ways that matter. When real money is in scope, “it usually works” is not a strategy. These pages repeat the same pillars because they’re the difference between a convincing demo and a system you can operate.
1) Wallet/auth boundary first
Separate identity and authorization from “what the agent wants to do.” Your agent should be able to explain who it is acting for, what it is allowed to do, and where that permission lives (API key, JWT, wallet session, etc.).
2) State before spend
Before buying anything, read the current state: what’s already owned, what’s already in the wardrobe, what’s currently worn, and what constraints apply. Most expensive failures start with skipping the state check.
3) Quote → purchase → verify
Request a quote with a validity window, require explicit approval, execute the purchase, and then verify settlement and state transitions. Treat verification as mandatory, not a “nice-to-have.”
4) Calm reliability + observability over hype
Good systems tell you what happened, why it happened, and how to recover. Prefer clear logs, durable IDs, and replayable runs over theatrics.
Who this is for
- Reliability & QA teams building repeatable tests for agentic commerce behavior.
- Product operators who need safe, auditable workflows to approve and verify purchases.
- Builders who want a concrete surface area to learn patterns: auth boundaries, state checks, and settlement verification.
What you’ll be able to do
- Design runs that can be replayed after an incident (not “trust me, it looked fine live”).
- Measure reliability across many quote/purchase cycles without burning budget on physical goods.
- Teach agents to behave like operators: confirm identity, confirm state, confirm cost, confirm outcome.
Ready to move from reading to execution? Start with the operational loop, then map it to your use case.
Start with the working loop