---
title: "AI Voice Agent Analytics: The KPIs That Actually Matter"
description: "The 15 KPIs that matter for AI voice agent operations — from answer rate and FCR to cost per successful resolution."
canonical: https://callsphere.ai/blog/ai-voice-agent-analytics-kpis-that-matter
category: "Technical Guides"
tags: ["AI Voice Agent", "Technical Guide", "Analytics", "KPIs", "Metrics", "Observability", "Operations"]
author: "CallSphere Team"
published: 2026-04-08T00:00:00.000Z
updated: 2026-05-06T01:02:47.284Z
---

# AI Voice Agent Analytics: The KPIs That Actually Matter

> The 15 KPIs that matter for AI voice agent operations — from answer rate and FCR to cost per successful resolution.

## If you are not measuring these, you are guessing

Voice agent dashboards tend to show whatever was easiest to build — total calls, total minutes, maybe sentiment. None of those tell you whether the agent is good at its job. This post lays out the 15 KPIs that actually matter for operating an AI voice agent and shows how to compute each one against a standard call log schema.

```
Every metric answers a question:
  • Did callers reach us?
  • Did the agent solve their problem?
  • How much did it cost?
  • Did anything go wrong?
```

## Architecture overview

```
┌────────────────────┐
│ Voice agent runtime│
└─────────┬──────────┘
          │ call events
          ▼
┌────────────────────┐
│ calls table (OLTP) │
└─────────┬──────────┘
          │ CDC / copy
          ▼
┌────────────────────┐
│ analytics store    │
│ (ClickHouse / BQ)  │
└─────────┬──────────┘
          │
          ▼
┌────────────────────┐
│ dashboards + alerts│
└────────────────────┘
```

## Prerequisites

- A `calls` table with at minimum: `call_id`, `started_at`, `ended_at`, `duration_sec`, `outcome`, `escalated`, `language`, `cost_cents`.
- A `call_turns` table with transcripts.
- A `call_events` table (or enum column) with outcomes like `resolved`, `escalated`, `abandoned`.

## The 15 KPIs

### 1. Answer rate

Percentage of inbound attempts that the agent actually picked up.

```mermaid
flowchart LR
    APP(["Agent or API"])
    SDK["OTel SDK
GenAI conventions"]
    COL["OTel Collector"]
    subgraph BACKENDS["Backends"]
        TR[("Traces
Tempo or Honeycomb")]
        MET[("Metrics
Prometheus")]
        LOG[("Logs
Loki or ELK")]
    end
    DASH["Grafana plus alerts"]
    PAGE(["Pager"])
    APP --> SDK --> COL
    COL --> TR
    COL --> MET
    COL --> LOG
    TR --> DASH
    MET --> DASH
    LOG --> DASH
    DASH --> PAGE
    style SDK fill:#4f46e5,stroke:#4338ca,color:#fff
    style DASH fill:#f59e0b,stroke:#d97706,color:#1f2937
    style PAGE fill:#dc2626,stroke:#b91c1c,color:#fff
```

```sql
SELECT
  COUNT(*) FILTER (WHERE status = 'answered') * 1.0 / COUNT(*) AS answer_rate
FROM calls
WHERE started_at >= now() - interval '7 days';
```

### 2. Time to first word

How long from ring to the first syllable of the agent's greeting.

### 3. Average handle time (AHT)

### 4. First-contact resolution (FCR)

```sql
SELECT
  COUNT(*) FILTER (WHERE outcome = 'resolved' AND NOT followup_required) * 1.0 / COUNT(*) AS fcr
FROM calls;
```

### 5. Escalation rate

### 6. Containment rate

Inverse of escalation — the percentage of calls fully handled by the agent.

### 7. Abandon rate

### 8. Booking rate (for scheduling verticals)

### 9. Sentiment score

Aggregate from the post-call pipeline.

### 10. Cost per successful resolution

```sql
SELECT
  SUM(cost_cents) / NULLIF(SUM(CASE WHEN outcome = 'resolved' THEN 1 ELSE 0 END), 0) AS cpsr
FROM calls;
```

### 11. STT word error rate (WER)

Sample 1% of calls, have humans transcribe, compare.

### 12. Tool call success rate

### 13. Hallucination flag rate

From the post-call QA pipeline.

### 14. CSAT (when available)

### 15. Latency p95

## Step-by-step walkthrough

### 1. Standardize the call log schema

```sql
CREATE TABLE calls (
  call_id TEXT PRIMARY KEY,
  started_at TIMESTAMPTZ NOT NULL,
  ended_at TIMESTAMPTZ,
  duration_sec INT,
  status TEXT NOT NULL,
  outcome TEXT,
  escalated BOOLEAN DEFAULT FALSE,
  followup_required BOOLEAN DEFAULT FALSE,
  language TEXT,
  cost_cents INT,
  agent_version TEXT
);
```

### 2. Compute metrics in batches

Run a 5-minute rollup job for dashboards and an hourly rollup for historical trends.

### 3. Set SLOs and alert on p95

### 4. Expose the metrics in an admin UI

```typescript
async function fetchKpis(from: string, to: string) {
  return await db.oneOrNone(
    "SELECT * FROM kpi_rollup WHERE period_start >= $1 AND period_end  95% for always-on agents; the rest are config errors or carrier outages.

### How do I avoid biasing the post-call LLM scorer?

Run it blind to agent version and spot-check with humans.

### Can I compare my agent to humans directly?

Only against matched caller intents and with the same KPI definitions.

## Next steps

Want a dashboard wired to real voice-agent KPIs? [Book a demo](https://callsphere.tech/contact), read the [technology page](https://callsphere.tech/technology), or see [pricing](https://callsphere.tech/pricing).

#CallSphere #Analytics #KPIs #VoiceAI #Observability #Metrics #AIVoiceAgents

---

Source: https://callsphere.ai/blog/ai-voice-agent-analytics-kpis-that-matter