Post-Call Metrics

Post-call metrics let you pull specific insights from conversations after they end. Define what you want to know — satisfaction scores, call outcomes, issue categories — and Atoms analyzes each call to fill in the answers.

Location: Left Sidebar → Post Call Metrics

Post-call metrics list — Post-call metrics dashboard

How It Works

You define metrics — What questions do you want answered about each call?
Call ends — Conversation completes normally
AI analyzes — Atoms reviews the transcript against your metrics
Data populated — Your metrics get filled in automatically
Access anywhere — View in logs, receive via webhook, export

Creating a New Metric

Click the Add Metrics + button to open the configuration panel. You’ll see two options:

Disposition Metrics

Templates

Build a custom metric from scratch. Fill in the Identifier, Data Type, and Prompt — see details below.

Use Add Another + to create multiple metrics at once.

Don’t forget to hit Save in the Disposition tab once you’re done.

Configuring a Metric

Each metric needs three things:

Field	Required	Description
Identifier	Yes	Unique name for this metric. Lowercase, numbers, underscores only.
Data Type	Yes	What kind of value: String, Number, or Boolean
Prompt	Yes	The question you want answered about the call

Identifier

This is the key used to reference the metric in exports, webhooks, and the API.

customer_satisfaction
call_outcome
follow_up_needed

Naming rules: Lowercase letters, numbers, and underscores only. No spaces or special characters.

Data Type

Type	Use for	Example values
String	Free text, categories	”resolved”, “escalated”, “billing issue”
Boolean	Yes/no questions	true, false
Integer	Whole numbers, scores	1, 5, 10
Enum	Fixed set of options	One of: “low”, “medium”, “high”
Datetime	Dates and times	”2024-01-15T10:30:00Z”

Prompt

This is the question the AI answers by analyzing the transcript. Be specific.

Good prompts:

“Did the agent acknowledge and respond to customer concerns effectively?”
“Rate customer satisfaction from 1 to 5 based on tone and words used.”
“What was the primary reason for this call? Options: billing, technical, account, other”

Vague prompts to avoid:

“Was it good?”
“Customer happy?”

Start with 3-5 metrics. Too many can slow analysis and clutter your data. Add more as you learn what insights matter most.

Example Metrics

Call Outcome

Satisfaction Score

Follow-Up Needed

Issue Category

Field	Value
Identifier	`call_outcome`
Data Type	String
Prompt	”What was the outcome of this call? Options: resolved, escalated, transferred, abandoned, callback_scheduled”

Configuring via API

Post-call metrics are set through the agent versioning flow: edit a draft’s config, publish it as a new version, and activate the version. There is no standalone post-call-analytics endpoint — configuration lives on the agent’s active version.

Full flow

1 import os
2 import requests
3 
4 API_KEY = os.environ["SMALLEST_API_KEY"]
5 AGENT_ID = "YOUR_AGENT_ID"
6 BASE = "https://api.smallest.ai/atoms/v1"
7 HEADERS = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"}
8 
9 # 1. Get the currently active version
10 agent = requests.get(f"{BASE}/agent/{AGENT_ID}", headers=HEADERS).json()
11 active_version_id = agent["data"]["activeVersionId"]
12 
13 # 2. Create a draft from that version
14 draft = requests.post(
15     f"{BASE}/agent/{AGENT_ID}/drafts",
16     headers=HEADERS,
17     json={"sourceVersionId": active_version_id, "draftName": "post-call-metrics-update"},
18 ).json()
19 draft_id = draft["data"]["draftId"]
20 
21 # 3. Write your post-call metrics config onto the draft
22 requests.patch(
23     f"{BASE}/agent/{AGENT_ID}/drafts/{draft_id}/config",
24     headers=HEADERS,
25     json={
26         "postCallAnalyticsConfig": {
27             "dispositionMetrics": [
28                 {
29                     "identifier": "call_resolved",
30                     "dispositionMetricPrompt": "Was the customer issue resolved by the end of the call?",
31                     "dispositionMetricType": "BOOLEAN",
32                 },
33                 {
34                     "identifier": "satisfaction_score",
35                     "dispositionMetricPrompt": "Rate the customer satisfaction from 1 to 5",
36                     "dispositionMetricType": "INTEGER",
37                 },
38                 {
39                     "identifier": "call_outcome",
40                     "dispositionMetricPrompt": "What was the outcome of the call?",
41                     "dispositionMetricType": "ENUM",
42                     "choices": ["resolved", "escalated", "callback_scheduled", "no_action"],
43                 },
44                 {
45                     "identifier": "summary",
46                     "dispositionMetricPrompt": "Provide a brief summary of the call",
47                     "dispositionMetricType": "STRING",
48                 },
49             ],
50             "useInternalAnalyticsModel": True,
51             "useReasoningModel": False,
52         }
53     },
54 ).raise_for_status()
55 
56 # 4. Publish the draft as a new version and activate it
57 requests.post(
58     f"{BASE}/agent/{AGENT_ID}/drafts/{draft_id}/publish",
59     headers=HEADERS,
60     json={"label": "Added post-call metrics", "activate": True},
61 ).raise_for_status()

Disposition metric schema

Each entry in dispositionMetrics takes four fields:

Field	Required	Description
`identifier`	Yes	Machine name. Lowercase letters, digits, and underscores only.
`dispositionMetricPrompt`	Yes	The question evaluated against the transcript.
`dispositionMetricType`	Yes	One of `STRING`, `BOOLEAN`, `INTEGER`, `ENUM`, `DATETIME`.
`choices`	Only when type is `ENUM`	Allowed values.

Two analytics flags apply globally:

Field	Default	Description
`useInternalAnalyticsModel`	`true`	Use the internal analytics model. When `false`, the agent’s own LLM evaluates metrics.
`useReasoningModel`	`false`	Route evaluation through the reasoning model — higher quality, higher latency/cost.

Reading current metrics

$ curl -H "Authorization: Bearer $SMALLEST_API_KEY" \
>   "https://api.smallest.ai/atoms/v1/agent/$AGENT_ID" \
>   | jq '.data._resolvedConfig.postCallAnalyticsConfig'

The active version’s metrics are surfaced under _resolvedConfig.postCallAnalyticsConfig. Each entry round-trips exactly as sent: identifier, dispositionMetricPrompt, dispositionMetricType, and choices (when the type is ENUM).

Conversation Logs

View metrics for individual calls

Analytics Dashboard

See aggregated trends across calls

Location: Left Sidebar → Post Call Metrics

How It Works

You define metrics — What questions do you want answered about each call?
Call ends — Conversation completes normally
AI analyzes — Atoms reviews the transcript against your metrics
Data populated — Your metrics get filled in automatically
Access anywhere — View in logs, receive via webhook, export

Creating a New Metric

Click the Add Metrics + button to open the configuration panel. You’ll see two options:

Disposition Metrics

Templates

Build a custom metric from scratch. Fill in the Identifier, Data Type, and Prompt — see details below.

Use Add Another + to create multiple metrics at once.

Don’t forget to hit Save in the Disposition tab once you’re done.

Configuring a Metric

Each metric needs three things:

Field	Required	Description
Identifier	Yes	Unique name for this metric. Lowercase, numbers, underscores only.
Data Type	Yes	What kind of value: String, Number, or Boolean
Prompt	Yes	The question you want answered about the call

Identifier

This is the key used to reference the metric in exports, webhooks, and the API.

customer_satisfaction
call_outcome
follow_up_needed

Naming rules: Lowercase letters, numbers, and underscores only. No spaces or special characters.

Data Type

Type	Use for	Example values
String	Free text, categories	”resolved”, “escalated”, “billing issue”
Boolean	Yes/no questions	true, false
Integer	Whole numbers, scores	1, 5, 10
Enum	Fixed set of options	One of: “low”, “medium”, “high”
Datetime	Dates and times	”2024-01-15T10:30:00Z”

Prompt

This is the question the AI answers by analyzing the transcript. Be specific.

Good prompts:

“Did the agent acknowledge and respond to customer concerns effectively?”
“Rate customer satisfaction from 1 to 5 based on tone and words used.”
“What was the primary reason for this call? Options: billing, technical, account, other”

Vague prompts to avoid:

“Was it good?”
“Customer happy?”

Start with 3-5 metrics. Too many can slow analysis and clutter your data. Add more as you learn what insights matter most.

Example Metrics

Call Outcome

Satisfaction Score

Follow-Up Needed

Issue Category

Field	Value
Identifier	`call_outcome`
Data Type	String
Prompt	”What was the outcome of this call? Options: resolved, escalated, transferred, abandoned, callback_scheduled”

Configuring via API

Full flow

1 import os
2 import requests
3 
4 API_KEY = os.environ["SMALLEST_API_KEY"]
5 AGENT_ID = "YOUR_AGENT_ID"
6 BASE = "https://api.smallest.ai/atoms/v1"
7 HEADERS = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"}
8 
9 # 1. Get the currently active version
10 agent = requests.get(f"{BASE}/agent/{AGENT_ID}", headers=HEADERS).json()
11 active_version_id = agent["data"]["activeVersionId"]
12 
13 # 2. Create a draft from that version
14 draft = requests.post(
15     f"{BASE}/agent/{AGENT_ID}/drafts",
16     headers=HEADERS,
17     json={"sourceVersionId": active_version_id, "draftName": "post-call-metrics-update"},
18 ).json()
19 draft_id = draft["data"]["draftId"]
20 
21 # 3. Write your post-call metrics config onto the draft
22 requests.patch(
23     f"{BASE}/agent/{AGENT_ID}/drafts/{draft_id}/config",
24     headers=HEADERS,
25     json={
26         "postCallAnalyticsConfig": {
27             "dispositionMetrics": [
28                 {
29                     "identifier": "call_resolved",
30                     "dispositionMetricPrompt": "Was the customer issue resolved by the end of the call?",
31                     "dispositionMetricType": "BOOLEAN",
32                 },
33                 {
34                     "identifier": "satisfaction_score",
35                     "dispositionMetricPrompt": "Rate the customer satisfaction from 1 to 5",
36                     "dispositionMetricType": "INTEGER",
37                 },
38                 {
39                     "identifier": "call_outcome",
40                     "dispositionMetricPrompt": "What was the outcome of the call?",
41                     "dispositionMetricType": "ENUM",
42                     "choices": ["resolved", "escalated", "callback_scheduled", "no_action"],
43                 },
44                 {
45                     "identifier": "summary",
46                     "dispositionMetricPrompt": "Provide a brief summary of the call",
47                     "dispositionMetricType": "STRING",
48                 },
49             ],
50             "useInternalAnalyticsModel": True,
51             "useReasoningModel": False,
52         }
53     },
54 ).raise_for_status()
55 
56 # 4. Publish the draft as a new version and activate it
57 requests.post(
58     f"{BASE}/agent/{AGENT_ID}/drafts/{draft_id}/publish",
59     headers=HEADERS,
60     json={"label": "Added post-call metrics", "activate": True},
61 ).raise_for_status()

Disposition metric schema

Each entry in dispositionMetrics takes four fields:

Field	Required	Description
`identifier`	Yes	Machine name. Lowercase letters, digits, and underscores only.
`dispositionMetricPrompt`	Yes	The question evaluated against the transcript.
`dispositionMetricType`	Yes	One of `STRING`, `BOOLEAN`, `INTEGER`, `ENUM`, `DATETIME`.
`choices`	Only when type is `ENUM`	Allowed values.

Two analytics flags apply globally:

Field	Default	Description
`useInternalAnalyticsModel`	`true`	Use the internal analytics model. When `false`, the agent’s own LLM evaluates metrics.
`useReasoningModel`	`false`	Route evaluation through the reasoning model — higher quality, higher latency/cost.

Reading current metrics

$ curl -H "Authorization: Bearer $SMALLEST_API_KEY" \
>   "https://api.smallest.ai/atoms/v1/agent/$AGENT_ID" \
>   | jq '.data._resolvedConfig.postCallAnalyticsConfig'

Conversation Logs

View metrics for individual calls

Analytics Dashboard

See aggregated trends across calls

1	import os
2	import requests
3
4	API_KEY = os.environ["SMALLEST_API_KEY"]
5	AGENT_ID = "YOUR_AGENT_ID"
6	BASE = "https://api.smallest.ai/atoms/v1"
7	HEADERS = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"}
8
9	# 1. Get the currently active version
10	agent = requests.get(f"{BASE}/agent/{AGENT_ID}", headers=HEADERS).json()
11	active_version_id = agent["data"]["activeVersionId"]
12
13	# 2. Create a draft from that version
14	draft = requests.post(
15	f"{BASE}/agent/{AGENT_ID}/drafts",
16	headers=HEADERS,
17	json={"sourceVersionId": active_version_id, "draftName": "post-call-metrics-update"},
18	).json()
19	draft_id = draft["data"]["draftId"]
20
21	# 3. Write your post-call metrics config onto the draft
22	requests.patch(
23	f"{BASE}/agent/{AGENT_ID}/drafts/{draft_id}/config",
24	headers=HEADERS,
25	json={
26	"postCallAnalyticsConfig": {
27	"dispositionMetrics": [
28	{
29	"identifier": "call_resolved",
30	"dispositionMetricPrompt": "Was the customer issue resolved by the end of the call?",
31	"dispositionMetricType": "BOOLEAN",
32	},
33	{
34	"identifier": "satisfaction_score",
35	"dispositionMetricPrompt": "Rate the customer satisfaction from 1 to 5",
36	"dispositionMetricType": "INTEGER",
37	},
38	{
39	"identifier": "call_outcome",
40	"dispositionMetricPrompt": "What was the outcome of the call?",
41	"dispositionMetricType": "ENUM",
42	"choices": ["resolved", "escalated", "callback_scheduled", "no_action"],
43	},
44	{
45	"identifier": "summary",
46	"dispositionMetricPrompt": "Provide a brief summary of the call",
47	"dispositionMetricType": "STRING",
48	},
49	],
50	"useInternalAnalyticsModel": True,
51	"useReasoningModel": False,
52	}
53	},
54	).raise_for_status()
55
56	# 4. Publish the draft as a new version and activate it
57	requests.post(
58	f"{BASE}/agent/{AGENT_ID}/drafts/{draft_id}/publish",
59	headers=HEADERS,
60	json={"label": "Added post-call metrics", "activate": True},
61	).raise_for_status()

$	curl -H "Authorization: Bearer $SMALLEST_API_KEY" \
>	"https://api.smallest.ai/atoms/v1/agent/$AGENT_ID" \
>	\| jq '.data._resolvedConfig.postCallAnalyticsConfig'

How It Works

Creating a New Metric

Disposition Metrics

Templates

Configuring a Metric

Identifier

Data Type

Prompt

Example Metrics

Call Outcome

Satisfaction Score

Follow-Up Needed

Issue Category

Configuring via API

Full flow

Disposition metric schema

Reading current metrics

Related

How It Works

Creating a New Metric

Disposition Metrics

Templates

Configuring a Metric

Identifier

Data Type

Prompt

Example Metrics

Call Outcome

Satisfaction Score

Follow-Up Needed

Issue Category

Configuring via API

Full flow

Disposition metric schema

Reading current metrics

Related