What Is a Contact Center Central Office?

What Is a Contact Center Central Office?

A contact center central office is a telecom switching site, physical or virtualized, that routes voice traffic between PSTN carriers, SIP trunks, and a contact-center voice platform. In AI voice deployments, it is the infrastructure layer that shapes the audio entering IVRs, human-agent desktops, and voice agents. FutureAGI treats it as an upstream production variable: teams do not evaluate the central office itself, but they must measure how its codec, jitter, and packet-loss behavior affects ASR accuracy, audio quality, and task completion.

Why It Matters in Production LLM and Agent Systems

Most AI voice teams never touch central-office configuration directly — but its behavior shapes the inputs every voice agent has to handle. Codec selection, jitter, packet loss, MOS score, and trunk-side caller-ID handling all happen below the voice agent, and degrade the audio that the agent’s ASR pipeline receives. A voice agent that benchmarks at 96% transcription accuracy in test conditions can drop to 84% in production because the central-office leg uses a low-bitrate codec on certain trunks.

The pain shows up unevenly. Telecom and infra teams see codec and trunk metrics. AI engineers see ASR error rate and low_confidence_audio events on a subset of calls. Operations sees CSAT and AHT. Customers hear a voice agent that “doesn’t understand” because the audio it received was not what the model trained on.

Unlike a standalone MOS dashboard, a voice-agent eval must connect telecom signal quality to ASR error, task completion, and escalation rate. In 2026 voice AI deployments, this surface is increasingly important. Voice agents are being shipped to enterprises with mixed telecom estates — some calls riding modern SIP trunks, others traversing legacy circuits — and the voice agent has to handle both. Trajectory-level evaluation that includes audio-quality signals is the only way to separate “the model got worse” from “the audio got worse” when overall quality dips.

How FutureAGI Handles Voice AI on Top of Central-Office Infrastructure

FutureAGI does not run central-office infrastructure. FutureAGI’s approach is to treat central-office behavior as an upstream cohort variable, not as a model metric. What it does is evaluate the AI voice agent and the audio it processes, regardless of the telecom path. traceAI’s traceAI-livekit integration captures every voice agent call — model calls, tool calls, transcript spans — and the audio file itself, time-aligned. From that trace, FutureAGI runs ASRAccuracy against ground-truth transcripts and AudioQualityEvaluator against the captured audio, producing per-call signals on both transcription quality and audio surface quality.

For pre-production testing, the simulate-sdk’s LiveKitEngine lets a team replay synthetic personas through their voice agent under controlled audio conditions — including degraded-audio scenarios that mimic central-office codec variation. Persona and Scenario define the test cases; the engine runs them at scale; TestReport aggregates transcripts and audio paths so the team can see whether ASR error rate spikes at a specific simulated codec.

A practical example: a healthcare voice-agent team finds that TaskCompletion drops on calls from a specific carrier. They pull the failing traces in FutureAGI, see that ASRAccuracy is 12 points below baseline on those calls, and confirm via AudioQualityEvaluator that the audio MOS score is also down. They escalate to telecom; the carrier admits a recent codec change on those trunks. The fix is not in the AI agent — but the AI eval pipeline is what surfaced the problem in two days instead of two months.

How to Measure or Detect It

The metrics relevant to AI voice agents on top of central-office infra:

• ASRAccuracy — transcript word-error-rate vs. ground truth; the canonical AI-side audio-quality proxy.
• AudioQualityEvaluator — direct audio surface evaluation, including MOS and clarity signals.
• TaskCompletion — call-level goal achievement; the integrating metric.
• Codec / jitter / packet-loss telemetry — owned by the telecom or CCaaS stack, not FutureAGI.
• eval-fail-rate-by-cohort — sliced by trunk or carrier to expose telecom-side regressions.

from fi.evals import ASRAccuracy, AudioQualityEvaluator

asr = ASRAccuracy().evaluate(audio=path, reference=transcript)
audio = AudioQualityEvaluator().evaluate(audio=path)
print(asr.score, audio.score)

Common Mistakes

• Blaming the model when the audio degrades. Without audio-quality signals, every voice regression looks like a model bug.
• Skipping per-trunk slicing. Aggregate ASR error hides codec-specific regressions on a single trunk or carrier.
• No simulation under degraded audio. Pre-production tests on clean audio overstate production performance.
• Treating telecom and AI as separate teams that never share metrics. Joint dashboards (trunk-side QoS plus AI-side ASR) catch regressions early.
• Ignoring audio capture in evaluation. Transcript-only evals miss whether the audio itself was the failure surface.