Agent evaluation for banking teams shipping AI
Agents in banking touch account data and money movement, and most teams have no record of what an agent actually did with either.
Every run scored for quality, performance and risk, and checked against the activity schema you define.
Every banking 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 banking
For banking risk, compliance, and engineering teams, the stakes are regulatory exposure, audit gaps, and customer-facing errors that surface before anyone internally notices.
A loan agent misreads a credit file or approves a transfer it should have flagged, and it surfaces as a customer complaint before your team catches it.
Did the agent move money, approve a limit, or change an account the way it was actually supposed to? Nobody can say for certain.
Teams can describe what an agent is supposed to do, but few can show what it actually did once it's calling three or four internal systems.
One source of truth for your banking 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
Claude Agent SDKSDKCore SDK · TS + PythonOTLPOpenTelemetry · closed toolsObserve. Evaluate. Act.
Every agent run in banking gets evaluated the same way, whether it's reading a credit file or reviewing a transaction. Each step below closes one of the problems above.
- Observe. Scored for quality on every run, before an underwriter sees it
- Evaluate. Checked against your activity schema before it counts as valid
- Act. Written to an immutable audit trail, queryable per instance
- SR 11-7
- FFIEC
- SOC 2
- GDPR (EU customers)
- DORA (EU operational resilience)
Built for everyone who owns banking AI.
The same record, read the way each team needs it.
Instrument the banking agents you already run — no gateway in the request path, no re-architecture.
For developers →Ship banking features without regressions — every run scored before a customer ever sees it.
For product teams →One portfolio view of every banking agent — its owner, cost and risk in a single place.
For heads of AI →Audit-ready evidence for every decision a banking agent makes, ready when a regulator asks.
Security & governance →Two agents banking teams are already shipping.
Hypothetical, but grounded in how banking teams deploy agents today.
Loan underwriting assistant
Low risk · 94/100A LangChain agent pulls a credit bureau report, cross-references it against the loan origination system, and drafts a risk tier and approval recommendation for a human underwriter to sign off on. Every recommendation is scored through quality assessment → before it reaches the underwriter.
- Credit bureau call, risk tier calculation, and draft recommendation each recorded as their own span
- Validated against an activity schema, so a malformed or out-of-range output is flagged
- Scored for quality and risk on every run
The team can show, for any single loan file, exactly what data the agent used and how it got to its recommendation.
Fraud investigation agent
Medium risk · 88/100A Claude Agent SDK agent reviews transaction patterns across a customer's recent activity, annotates suspicious transfers with its reasoning, and surfaces a ranked case list in the fraud team's queue for a human analyst to action. Each ranked case carries a risk score → so the queue surfaces the transactions that matter most first.
- Every transaction reviewed and every annotation written captured as a span with its own schema
- Annotations checked to confirm they cite a real transaction, not a fabricated one
- A configurable risk profile weights false positives against missed cases
The fraud team can trace a flagged case back to the exact transactions the agent used, and stop a single run without pulling the whole system offline.
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 banking 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 banking agents without re-architecting?
Can we bring our own banking systems and metrics into a run?
How does evaluation work on each run?
How is this different from an observability tool?
Where does our banking 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.