# AI Call QA: Evaluate After the Call Ends

> Why AI call evaluation belongs after hang-up, not in the live call, and how to track outcome, AI suspicion, script violations and score for every agent.

Source: https://edesy.in/blog/evaluate-ai-calls-after-hang-up-qa-2026

**Scoring AI calls is only useful if every call is scored the same way, by something that isn't
also trying to hold the conversation.** In one deployment, a CBSE exam-prep book publisher runs
about a dozen evaluation fields on its teacher calls. Getting there took two fixes: moving evaluation
out of the live call, and making sure every agent that ran calls had it attached. This post covers
both, and the fields worth tracking.

> **Set up call QA from day one:** [Start free on Edesy](https://voice-agent.edesy.in/?utm_source=edesy.in&utm_medium=blog&utm_campaign=evaluate-ai-calls-after-hang-up-qa-2026) — Rs 50 free credit, no demo needed.

## Lesson 1: evaluate after hang-up, not during the call

The first version put the evaluation fields into the live call's data, which meant the agent was
asked to judge its own conversation while having it. Three problems followed:

- **A second job.** The agent's instructions now included "decide if the caller suspects AI" and
  "note script violations". Some of that leaked into how it spoke.
- **Partial information.** Mid-call, the agent can't know how the call ends.
- **Slower turns.** More instructions and more to track on every reply.

The fix was simple: keep the live agent focused on the conversation, and run evaluation **after the
call ends**, on the full transcript. Post-call extraction sees the whole call, runs once, and never
affects what the caller hears.

## Lesson 2: every agent that runs calls needs the template

Teams often add a second agent: a follow-up call, a different language, a new campaign. In the same
deployment, the follow-up agent ran its first calls with **no evaluation at all**, because the
template was scoped to the original agent. Nobody noticed until they looked for the numbers.

Checklist whenever you add or clone an agent:

- attach an evaluation template before the first real call;
- run five test calls and confirm every field fills;
- check that unanswered dials still produce a row.

## The fields worth tracking

| Field | Values | What it tells you |
|---|---|---|
| Call outcome | e.g. approved, not interested, wrong person, callback, dropped early, hostile | Your funnel, one word per call |
| Drop reason | Why a failed call failed | Which objection or moment loses people |
| AI suspected | Yes / no | Whether the caller realised it was an AI |
| AI suspicion stage | Opening, qualification, offer, close | Which line gives it away |
| Script violation | Yes / no + short note | Prompt bugs, before customers complain |
| Qualification verdict | Qualified / not qualified / unclear | Whether the person met your criteria |
| Call score | 1 to 5 | A quick sort for listening to the worst calls first |

Two of these deserve a closer look.

**AI suspicion and its stage.** "Did they suspect AI?" alone isn't actionable. "They suspected at
the opening line" is: rewrite the greeting. Being honest when asked is the right policy; the goal is
a natural conversation, not hiding what it is.

**Script violations.** One real example: the agent occasionally answered its own question in the
caller's voice ("Aap kaunsi class padhate hain? ... Class 8, okay"). A violation field catches this
within a day, instead of after a week of bad data.

## Reviewing the results

A weekly routine that works:

1. Sort by call score, lowest first, and listen to ten calls.
2. Filter AI suspected = yes and group by stage. Fix the most common stage.
3. Read every script violation.
4. Compare outcomes week on week after each prompt change.

For a deeper look at failures, see
[why AI calls fail: drop reasons by script stage](https://edesy.in/blog/why-ai-calls-fail-drop-reason-analytics-2026).
For writing the field descriptions, see
[score every AI call automatically](https://edesy.in/blog/score-every-ai-call-automatically-2026).

## Try it

Post-call extraction is included on Edesy. Results appear in call history and dashboards, and can be
exported to CSV or Google Sheets or sent to your CRM by webhook.
[Create a free account](https://voice-agent.edesy.in/?utm_source=edesy.in&utm_medium=blog&utm_campaign=evaluate-ai-calls-after-hang-up-qa-2026),
or read [how a publisher verifies teachers with AI calls](https://edesy.in/ai-voice-assistant/case-studies/publisher-teacher-verification-calls).

## Related reading

- [Your first week with a new AI voice agent](https://edesy.in/blog/first-week-new-ai-voice-agent-monitoring-2026)
- [Call center analytics: AI sentiment and QA](https://edesy.in/blog/call-center-analytics-ai-sentiment-qa-2026)
- [Follow-up calls after free samples ship](https://edesy.in/blog/sample-delivery-follow-up-calls-ai-2026)
- [Hinglish STT, acronyms and keyword checks](https://edesy.in/blog/hinglish-stt-acronyms-keyword-qualification-2026)

## Frequently Asked Questions

### Should an AI voice agent evaluate its own call while it is talking?

No. Asking the live agent to also score the call gives it a second job, adds instructions it may act on mid-conversation, and slows replies. Run evaluation after the call ends, on the full transcript.

### What should be evaluated on every AI call?

A one-word outcome, the drop reason if the call failed, whether the caller suspected they were talking to an AI and at which stage, any script violation, and an overall call score. Each becomes a column you can filter.

### What is a script violation on an AI call?

Anything the agent should never do: inventing a price, skipping a mandatory question, promising something outside policy, or answering its own question on the caller's behalf. Flagging these finds prompt problems quickly.

### Why do some agents show no analytics?

Usually because the evaluation template is attached to one agent only. When a team clones or adds an agent for a follow-up campaign, the new agent runs calls with nothing extracted. Attach evaluation to every agent that runs real calls.

### Where do the evaluation results go?

Into the call history as columns and into dashboards, and they can be exported to CSV or Google Sheets or sent to your CRM by webhook.