What Are Contact Center Call Logs?

What Are Contact Center Call Logs?

Contact center call logs are structured records of every inbound and outbound call: timestamp, ANI (caller number), DNIS (called number), queue, agent ID, hold time, talk time, after-call work, disposition code, and a pointer to the call recording or transcript. They are exported from the ACD or CCaaS platform as the raw event log behind staffing, billing, QA, and compliance reporting. In an AI-augmented stack, these same log entries become the dataset that voice-agent evaluation, transcript audit, and resolution scoring run against in FutureAGI.

Why It Matters in Production LLM and Agent Systems

Call logs are the spine of a contact center. Workforce planners forecast volume from them. Billing reconciles per-minute charges from them. QA samples calls for review from them. Compliance audits dispute resolution from them. Every downstream contact-center process depends on the log being accurate, timely, and complete.

The LLM era adds new consumers. Conversational analytics modules read call logs to bucket sessions by intent and outcome. AI scorecards grade calls picked from log samples. Voice-agent evaluation pipelines pull every IVR session into a dataset. Auto-summarization tools generate post-call notes attached to the log row. Each of those LLM consumers introduces a new failure mode — a wrong intent label, a hallucinated summary, a missed escalation flag — and each needs evaluation.

The pain shows up in three places. A QM team finds that 8% of LLM-generated summaries on log rows contradict the actual transcript. A compliance audit discovers that calls flagged “resolved” in the disposition code were marked “unresolved” by the manual reviewer in 14% of sampled cases. A platform owner realizes that their AI voice-agent fleet is generating calls without proper log entries because the new IVR runtime didn’t write back to the legacy ACD. By 2026, call logs need to be evaluated as a data product, not just exported.

How FutureAGI Handles Contact Center Call Logs

FutureAGI ingests call logs as a Dataset, with one row per call and columns for timestamp, agent ID, transcript, audio path, disposition, and any LLM-generated fields like summary or auto-resolution flag. Evaluators attached to the dataset score the AI layer. ASRAccuracy grades the transcription quality against a reference subset. ConversationResolution scores whether the call’s stated user goal was reached. AudioQualityEvaluator checks the audio path for codec artifacts that degrade ASR. CustomerAgentConversationQuality produces a composite quality score for QA sampling.

A concrete example: a 2,500-seat banking contact center exports a daily call log of 180,000 rows, 12,000 of which are AI-IVR calls. The team loads them into a FutureAGI Dataset via Dataset.add_evaluation with ConversationResolution attached. The dashboard splits AI-IVR resolution score by intent (balance inquiry, dispute, password reset). When dispute resolution drops 11 points week-over-week, the trace view (via traceAI-livekit) shows the agent is being interrupted by barge-in and not recovering — a turn-detection regression. The fix is in the agent runtime; the detection lives in the call log evaluation pipeline.

For human queues, FutureAGI scores the LLM modules running on the logs (auto-summary, sentiment, intent label) but does not replace the WFM analytics that consume them.

How to Measure or Detect It

Call-log evaluation depends on linking metadata, transcript, and audio:

• Log completeness — share of calls with all required fields populated; spike in nulls = pipeline regression.
• ASRAccuracy — sample 5% of calls with reference transcripts; track mean WER per language and accent.
• ConversationResolution — score the AI-IVR portion of the log against user-stated goals.
• AudioQualityEvaluator — flag audio-path issues that corrupt every downstream LLM consumer.
• Auto-summary Faithfulness — score generated summaries against the source transcript.
• Disposition-code accuracy — agreement rate between agent-set disposition and human review.

from fi.evals import ASRAccuracy, ConversationResolution

asr = ASRAccuracy()
resolution = ConversationResolution()

asr_result = asr.evaluate(
    audio_path="/recordings/call-12345.wav",
    reference_text=ground_truth_transcript,
)
print(asr_result.score)

Common Mistakes

• Treating the log as ground truth. The log is what the system recorded; sample manually to validate.
• Skipping AI-IVR call entries. Some new voice-agent platforms do not write to the legacy ACD; reconcile two log sources during migration.
• Evaluating only completed calls. Abandoned and short-call rows carry the dropout signal; include them.
• No PII redaction before dataset upload. Raw transcripts contain account numbers, addresses, names; redact before evaluation.
• One ASR threshold for every language. WER thresholds need per-language baselines; a 0.12 WER may be excellent in one language and poor in another.