Agent evaluation for manufacturing teams shipping AI
An agent recommending a maintenance action or flagging a defect is one step from a physical outcome on the floor, and most teams cannot trace a bad call back to the data that caused it.
Every run scored for quality, performance and risk, and checked against the activity schema you define.
Every manufacturing 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 manufacturing
For plant leads, quality managers, and the engineering teams behind them, the stakes are unplanned downtime, defective units reaching a customer, and no record of what an agent based a recommendation on.
A predictive maintenance agent that misreads a sensor trend does not just produce a wrong number, it can lead to unplanned downtime or a defective unit reaching a customer.
An agent's call becomes a work order, a line stoppage, or a quality hold, triggered on a misread of the underlying sensor or production data.
Most plants can describe what a maintenance or quality agent is supposed to do, but few can show which sensor readings actually drove a specific work order.
One source of truth for your manufacturing 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.
Semantic Kernel
LangChainSDKCore SDK · TS + PythonOTLPOpenTelemetry · closed toolsObserve. Evaluate. Act.
Every agent run on the floor is observed span by span, evaluated against the data that triggered it, and held before it acts. Each step below closes one of the problems above.
- Observe. Scored for quality on every run, before a technician sees it
- Evaluate. Checked against the sensor pattern that triggered it, before it reaches the line
- Act. Written to an immutable audit trail, queryable per machine
- OSHA (US workplace safety)
- ISO 9001
- Sector-specific safety codes
- SOC 2
Built for everyone who owns manufacturing AI.
The same record, read the way each team needs it.
Instrument the manufacturing agents you already run — no gateway in the request path, no re-architecture.
For developers →Ship manufacturing features without regressions — every run scored before a customer ever sees it.
For product teams →One portfolio view of every manufacturing agent — its owner, cost and risk in a single place.
For heads of AI →Audit-ready evidence for every decision a manufacturing agent makes, ready when a regulator asks.
Security & governance →Two agents manufacturing teams are already shipping.
Hypothetical, but grounded in how manufacturing teams deploy agents today.
Predictive maintenance agent
Low risk · 91/100A Semantic Kernel agent monitors sensor data from production equipment, identifies patterns that precede a failure, and creates a work order in the maintenance system for a technician to review. Every work order is scored through quality assessment → before it reaches the floor.
- Each sensor reading the agent evaluates and each work order it creates recorded as its own span
- Validated against an activity schema that checks the stated cause actually matches the sensor pattern that triggered it
- Scored for quality and risk on every run
A maintenance lead can trace a specific work order back to the exact sensor readings that triggered it, instead of taking the recommendation on faith.
Quality inspection agent
Medium risk · 86/100A LangChain agent reviews production line vision and sensor data to flag potential defects, logging each flagged unit to the quality management system for a human inspector to confirm. Each flagged unit carries a risk score → so the inspector queue surfaces the units that matter most first.
- Every unit the agent evaluates and every defect it flags captured as a span
- Flags checked to confirm they cite specific sensor or image data rather than an unsupported judgment
- A configurable risk profile weights defect types by severity
A quality inspector can see exactly what production data supported a specific defect flag before confirming or overriding it.
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 manufacturing 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 manufacturing agents without re-architecting?
Can we bring our own manufacturing systems and metrics into a run?
How does evaluation work on each run?
How is this different from an observability tool?
Where does our manufacturing 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.