Agent evaluation for telecommunications teams shipping AI
An agent adjusting a bill or changing a network configuration is touching protected customer data and live service, and most teams cannot trace either back to a cause.
Every run scored for quality, performance and risk, and checked against the activity schema you define.
Every telecommunications 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 telecommunications
For telecom billing, network operations, and platform engineering teams, the stakes are protected customer records, live service changes, and credits that reach customers minutes before anyone internally reviews them.
Billing and network agents reach protected customer account and usage records, and most teams have no record of what each one read.
A credit or a traffic reroute based on data that was already stale reaches customers within minutes.
Few operators can show, for a specific credit or configuration change, exactly what customer or network data drove it.
One source of truth for your telecommunications 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 billing and network run is observed span by span, evaluated against the schema it was scoped to touch, and held before a credit or a configuration change commits. Each step below closes one of the problems above.
- Observe. Every lookup recorded as its own span, queryable per account
- Evaluate. Checked against its activity schema and telemetry window before it commits
- Act. Written to an immutable audit trail, queryable per credit or config change
- FCC CPNI rules
- GDPR (international operators)
- State telecom regulations
- SOC 2
Built for everyone who owns telecommunications AI.
The same record, read the way each team needs it.
Instrument the telecommunications agents you already run — no gateway in the request path, no re-architecture.
For developers →Ship telecommunications features without regressions — every run scored before a customer ever sees it.
For product teams →One portfolio view of every telecommunications agent — its owner, cost and risk in a single place.
For heads of AI →Audit-ready evidence for every decision a telecommunications agent makes, ready when a regulator asks.
Security & governance →Two agents telecommunications teams are already shipping.
Hypothetical, but grounded in how telecommunications teams deploy agents today.
Billing dispute agent
Medium risk · 92/100A CrewAI agent reviews a customer's billing history and plan details when a dispute comes in, and issues a credit automatically for discrepancies under a set threshold, escalating larger disputes to a billing specialist.
- Billing history lookup, discrepancy calculation, and credit action each recorded as their own span
- Validated against an activity schema that confirms the credit amount matches the calculated discrepancy
- Scored for quality and risk on every run
Finance can trace every automatic credit back to the specific billing discrepancy that justified it, not just the total issued.
Network operations agent
High risk · 87/100A LangChain agent monitors network telemetry and recommends configuration changes, such as traffic rerouting, to a network engineer, who approves before the change pushes to live infrastructure.
- Every telemetry reading evaluated and every recommendation made captured as its own span
- Checked against an activity schema that confirms the recommendation is grounded in telemetry from the last defined window, not stale data
- A configurable risk profile flags a recommendation affecting a high-priority segment for mandatory engineer approval
A network engineer can confirm a recommended change is based on current telemetry before approving it, not after an outage.
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 telecommunications 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 telecommunications agents without re-architecting?
Can we bring our own telecommunications systems and metrics into a run?
How does evaluation work on each run?
How is this different from an observability tool?
Where does our telecommunications 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.