Agent evaluation for marketing teams shipping AI
An agent publishing under your brand or touching a customer segment can do real reputational and regulatory damage in one bad run, and most teams find out after it is live.
Every run scored for quality, performance and risk, and checked against the activity schema you define.
Every marketing 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 marketing
For the CMOs, content leads, and engineering teams behind a content or campaign agent, the stakes are the customer data the agent reaches for targeting and every claim it publishes under the company name.
Content and campaign agents reach customer attributes for targeting, and the team needs a record of which ones.
A wrong claim goes out under the company name, to a wide audience, before anyone reads it.
Few teams can show, for a published post, where each claim actually came from.
One source of truth for your marketing 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.
A record of what the agent did, and a check before it goes further. Each step below closes one of the problems above.
- Observe. Every attribute read recorded as its own span
- Evaluate. Each claim checked against the source document it was pulled from
- Act. Written to an immutable audit trail, queryable per campaign
- FTC advertising and AI disclosure guidance
- CAN-SPAM / CASL
- GDPR / CCPA
- SOC 2
Built for everyone who owns marketing AI.
The same record, read the way each team needs it.
Instrument the marketing agents you already run — no gateway in the request path, no re-architecture.
For developers →Ship marketing features without regressions — every run scored before a customer ever sees it.
For product teams →One portfolio view of every marketing agent — its owner, cost and risk in a single place.
For heads of AI →Audit-ready evidence for every decision a marketing agent makes, ready when a regulator asks.
Security & governance →Two agents marketing teams are already shipping.
Hypothetical, but grounded in how marketing teams deploy agents today.
Content generation agent
Low risk · 88/100A CrewAI agent drafts blog posts and social copy by pulling product facts from an internal knowledge base and brand voice guidelines from a style doc, publishing drafts into a CMS queue for editorial review.
- Each fact the agent pulls and each draft it produces recorded as its own span
- Validated against an activity schema that checks a claimed statistic or feature detail against the source document it was pulled from
- Scored for quality and risk on every run
An editor sees which source document backs a specific claim before the post publishes, not after a customer asks.
Campaign segmentation agent
Medium risk · 84/100A LangChain agent analyzes customer data in a CDP to build audience segments and recommends budget shifts across channels, with a marketer approving segment definitions before a campaign sends.
- Every segment the agent builds and every budget recommendation it makes captured as a span
- Each segment checked against its activity schema: whether the segment logic used only approved customer attributes
- A configurable risk profile flags a segment definition that touches a restricted data field
A marketer confirms a segment used only approved customer attributes before the campaign sends, not after it landed.
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 marketing 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 marketing agents without re-architecting?
Can we bring our own marketing systems and metrics into a run?
How does evaluation work on each run?
How is this different from an observability tool?
Where does our marketing 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.