Agent evaluation for retail and ecommerce teams shipping AI
An agent issuing a refund or changing a price is acting on customer money and trust at scale, and most teams cannot show why any single decision happened.
Every run scored for quality, performance and risk, and checked against the activity schema you define.
Every retail and ecommerce agent run gets watched, evaluated for quality and risk, and checked against the rules you set. One record answers what any agent did, with evidence ready for review.
What's at stake in retail and ecommerce
For retail support, pricing, and finance teams, the stakes are a refund issued against the wrong order, a price pushed live that should have been held, and no record of which data authorized either.
Agents reach order history and payment records to make a call, and nothing shows which records a given run actually pulled.
A refund, a price change, or an order cancellation lands on a customer the moment it executes, with no step in between.
Few teams can show, for a specific refund or price change, exactly what order or inventory data the agent used to make that call.
One source of truth for your retail and ecommerce agents.
Instrument the agent frameworks you build on, and ingest the systems those agents touch as custom spans. Every run, score and signal lands in one place.
CrewAI
LangChainSDKCore SDK · TS + PythonOTLPOpenTelemetry · closed toolsObserve. Evaluate. Act.
Every retail agent run is observed span by span, evaluated against the order or price band it references, and acted on before the money moves. Each step below closes one of the problems above.
- Observe. Every lookup recorded as its own span, tied to the order it read
- Evaluate. Checked against your activity schema before it reaches the customer
- Act. Written to an immutable audit trail, queryable per order
- PCI DSS
- GDPR / CCPA
- FTC pricing and advertising guidance
- SOC 2
Built for everyone who owns retail and ecommerce AI.
The same record, read the way each team needs it.
Instrument the retail and ecommerce agents you already run — no gateway in the request path, no re-architecture.
For developers →Ship retail and ecommerce features without regressions — every run scored before a customer ever sees it.
For product teams →One portfolio view of every retail and ecommerce agent — its owner, cost and risk in a single place.
For heads of AI →Audit-ready evidence for every decision a retail and ecommerce agent makes, ready when a regulator asks.
Security & governance →Two agents retail and ecommerce teams are already shipping.
Hypothetical, but grounded in how retail and ecommerce teams deploy agents today.
Refund and returns agent
Low risk · 93/100A CrewAI agent handles customer refund requests by pulling the order history and payment record, and issuing a refund automatically for requests under a set threshold, escalating anything larger to a support lead.
- Order lookup, threshold check, and refund action each recorded as their own span
- Validated against an activity schema that confirms the refund amount matches the original order value
- Scored for quality and risk on every run
Finance can reconcile every automatic refund against the exact order data that authorized it, instead of trusting the total.
Dynamic pricing agent
Medium risk · 88/100A LangChain agent adjusts product prices based on demand signals and competitor data, pulling from the inventory and pricing systems and pushing approved changes live within a configured band.
- Every price recommendation captured as a span alongside the demand and competitor data behind it
- Checked against an activity schema that tests the new price against the configured band, cost, and current inventory
- A configurable risk profile flags a proposed change outside that band for manual approval
A pricing team can see exactly which demand and competitor data justified a live price change, and catch an outlier before it publishes.
Runs stay isolated — terminate one without touching the rest of your fleet via the kill switch → · Prefactor vs. observability tools →
The problems this solves
Agents fail quietly and the first signal is a complaint, not an alert.
How it gets caught →Stuck at POCThe pilot worked; sign-off takes months because risk has no evidence.
How it gets caught →No kill switchWhen an agent misbehaves, nothing can stop it short of stopping everything.
How it gets caught →See agent evaluation on your own retail and ecommerce agents
Book a demo and we'll walk through span-level scoring and audit evidence on a fleet like yours.
Unified performance platform for agents, authentication, and risk management
Frequently asked questions
Can Prefactor instrument our existing retail and ecommerce agents without re-architecting?
Can we bring our own retail and ecommerce systems and metrics into a run?
How does evaluation work on each run?
How is this different from an observability tool?
Where does our retail and ecommerce data live?
Related glossary terms
See how every agent performs, and make it better
Prefactor helps teams observe, evaluate, and improve their AI agents in production, across every framework and provider.