How is call recording different from call monitoring?

Recording is the storage layer — it captures audio and transcripts. Monitoring is the analytical layer above the recording, scoring quality, compliance, and outcomes against the captured data.

How does FutureAGI use call recordings?

FutureAGI ties each recording to its OpenTelemetry trace, so engineers can replay a call alongside the LLM input/output, tool calls, and eval scores. ASRAccuracy and AudioQualityEvaluator score the recordings directly.

Call Recording Definition & FutureAGI Guide (2026)

Q: What is call recording?

Call recording is the automated capture and storage of audio from a call, usually paired with transcripts, span IDs, metadata, and consent flags so the call can be reviewed, audited, or replayed later.

What Is Call Recording?

Call recording is the automated capture and storage of call audio, normally alongside aligned transcripts, span IDs, agent metadata, and consent state. In an AI voice stack, the recording is evidence for downstream evaluators and trace replay, not just a compliance artifact. FutureAGI treats call recordings as trace-linked artifacts: if a recording is not joined to its OpenTelemetry trace, consent flags, and word-level timestamps, engineers cannot reliably score it, replay a customer dispute, or debug a model regression.

Why It Matters in Production LLM and Agent Systems

Recordings are the ground truth most production teams forget to budget for. Without them, an AI quality regression has no audit trail. A customer who claims the agent gave wrong advice cannot be answered. A compliance team asked to prove a banned topic was never discussed has only sample-based confidence. A product team trying to debug a 3% rise in escalation rate is reduced to staring at disposition codes.

The pain compounds in agent stacks. A voice agent’s behavior depends on a chain of model calls, tool calls, and retrieval calls — none of which are visible in the audio alone. Replaying a call requires the audio plus the trace plus the eval scores, all aligned. Teams that store audio in one bucket, transcripts in another, and OTel traces in a third spend hours per incident reconstructing context.

In 2026, regulators expect richer recording state too: consent provenance, redaction status, retention class, and PII scrub logs. EU AI Act high-risk classifications and HIPAA voice-AI deployments demand the recording layer track who recorded what, why, and for how long. A recording stack that just dumps WAV files to object storage will not survive an audit.

How FutureAGI Handles Call Recording

FutureAGI’s approach is to treat call recording as trace-linked evidence, not a standalone media file. FutureAGI does not own the recording layer — that lives in voice infrastructure such as LiveKit, Pipecat, or Twilio. Unlike Twilio Call Recording alone, which proves that audio exists, the FutureAGI workflow ties each recording to spans, consent metadata, and eval results so an engineer can debug model behavior.

Concretely: an AI voice agent instrumented with the livekit traceAI integration emits OTel spans for STT, LLM, tool calls, and TTS. The recording’s URI and consent flag are stored as span_attributes on the root span. FutureAGI’s UI uses that link to play audio inline next to the trace timeline, transcripts, eval scores, and tool calls. When ConversationResolution flags a call as failed, the engineer hits “play from minute 1:42” and watches what happened, with the LLM’s exact prompt and response visible alongside the audio.

For evaluation, ASRAccuracy runs against the STT span output paired with a reference transcript and returns word-error-rate; AudioQualityEvaluator scores the recording for noise, jitter, and packet loss; CaptionHallucination flags transcripts where the ASR output contains words the audio did not. The same recording feeds Dataset ingestion for golden-set construction: pick the 200 best-and-worst calls of a week and version them as a regression dataset before the next model rollout.

Consent and redaction are first-class: PII evaluation runs on transcripts before audio is shared with non-privileged users, and the trace stores the redaction state so audit queries can filter on it.

How to Measure or Detect It

Recording quality is measured at three layers — capture, alignment, and analytical readiness:

Recording-coverage rate: percentage of calls with full audio, transcript, and trace; a missing artifact is a debugging blind spot.
fi.evals.ASRAccuracy: returns word-error-rate per ASR span, validating recording-to-transcript alignment.
fi.evals.AudioQualityEvaluator: returns a 0–1 score per audio span; surfaces lossy capture or codec artifacts.
Word-level-timestamp drift: dashboard signal for the gap between audio playhead and transcript word offsets; large drift breaks replays.
Consent-flag completeness: percentage of recordings with valid consent provenance metadata; below 100% in regulated regions is a compliance issue.
PII-redaction rate: how often PII evaluation finds unredacted PII in transcripts before sharing.

from fi.evals import ASRAccuracy

asr = ASRAccuracy()
result = asr.evaluate(
    input="reference: I'd like to schedule a delivery for May 12th.",
    output="hypothesis: I'd like to schedule a delivery for May 12."
)
print(result.score, result.reason)

Common Mistakes

Storing audio without the trace. A recording detached from its trace cannot be replayed with eval context, and debugging takes hours longer.
Skipping consent metadata. A recording without provenance fails audit, even if the audio is otherwise fine.
Not validating ASR alignment continuously. Word-error-rate drifts as accents, codecs, and noise patterns shift; static spot-checks miss it.
Treating recordings as cold storage. Active eval pipelines need fast retrieval; if your recording layer takes 30 seconds per fetch, you cannot run continuous evaluation.
Mixing redacted and raw recordings. Compliance teams need a clean separation; one shared bucket with mixed redaction state is a leak waiting to happen.