Agent evaluation for real estate teams shipping AI
An agent screening a tenant or drafting a listing is touching a high-value transaction and a fair housing standard that gets tested in court, and most teams cannot show how it reached a decision.
Every run scored for quality, performance and risk, and checked against the activity schema you define.
Every real estate 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 real estate
For the brokers, leasing agents, and engineering teams behind a screening or listing agent, the stakes are a fair housing standard that gets tested in court and the applicant data the agent can reach on its way to a decision.
A listing that reads as steering, or a screen on a protected factor, is exposure that predates AI.
A decline nobody can explain in fair housing terms is the failure mode that reaches an inquiry.
Few teams can show, for a given applicant, which criterion the recommendation actually used.
One source of truth for your real estate 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.
LangChain
CrewAISDKCore SDK · TS + PythonOTLPOpenTelemetry · closed toolsObserve. Evaluate. Act.
A record of what the agent used, and a check before it goes further. Each step below closes one of the problems above.
- Observe. Every factor the agent used recorded as its own span
- Evaluate. Each factor checked against the property's published criteria
- Act. Written to an immutable audit trail, queryable per applicant and per listing
- Fair Housing Act
- ECOA (credit-based screening)
- State real estate licensing and disclosure law
- GDPR (international transactions)
- SOC 2
Built for everyone who owns real estate AI.
The same record, read the way each team needs it.
Instrument the real estate agents you already run — no gateway in the request path, no re-architecture.
For developers →Ship real estate features without regressions — every run scored before a customer ever sees it.
For product teams →One portfolio view of every real estate agent — its owner, cost and risk in a single place.
For heads of AI →Audit-ready evidence for every decision a real estate agent makes, ready when a regulator asks.
Security & governance →Two agents real estate teams are already shipping.
Hypothetical, but grounded in how real estate teams deploy agents today.
Tenant screening assistant
High risk · 87/100A LangChain agent reviews rental applications against a property's published screening criteria, income and credit thresholds, and drafts a recommendation for a leasing agent to approve or decline.
- Each application review recorded as its own span, with every factor the agent weighed attached to it
- Validated against an activity schema that confirms the recommendation used only the published screening criteria, not an unpublished or protected factor
- Scored for quality and risk on every run
For any declined applicant, a leasing agent can show exactly which published criterion the recommendation used.
Listing description agent
Medium risk · 90/100A CrewAI agent drafts property listing descriptions from structured property data, and a listing coordinator reviews the draft before it publishes to a listing platform.
- Every draft the agent produces captured as its own span
- Validated against an activity schema and a language screen that flags terms with a history of fair housing concern, before a coordinator sees the draft
- A draft that repeats flagged language can be held before it reaches the listing platform
A coordinator sees a flagged term before the listing publishes, with the flag and its resolution on the record.
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 real estate 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 real estate agents without re-architecting?
Can we bring our own real estate systems and metrics into a run?
How does evaluation work on each run?
How is this different from an observability tool?
Where does our real estate 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.