Post call analysis

Post call analysis collects insights, summaries, and structured data after conversations. For example, for agents that performs a job interview, you may collect answers to all questions asked during the interview. Or, you can evaluate user sentiment during the call, perform call summarization etc.

Collected information may be consumed via one of the following ways:

It can be sent to the WebHook URL specified by customer.
It can be seen in Logs screen - the 'post_call_analysis' log entry at the end of the conversation.
It can be accessed via REST API at /ai-framework-management/api/v1/accounts/{account_id}/conversations/{conversation_id}.

Modes of operation

Post call analysis can operate in one of the following modes:

Structured data extraction
Call summarization
Conversation transcript

Structured data extraction

Use this mode when you want AI Agent to extract specific information from the conversation.

To configure this:

Open the Post Call Analysis configuration screen.
Define one or more parameters. For each parameter, specify:
- Name – a unique name.
- Description – detailed guidance on what data should be captured for this parameter.
- Type – the expected data type (e.g., string, number, boolean).

3. Ensure the Post Call Analysis prompt is configured. By default, it is optimized for this mode but can be customized if necessary.

Do not include parameter-specific extraction instructions directly in the prompt. Instead, use each parameter’s Description field to provide precise guidance.

Call summarization

Use this mode when you need a structured or insightful summary of the conversation.

To configure this:

Leave the Parameters list empty.
Edit the Post Call Analysis prompt to include instructions for summarizing the conversation.
Ensure your prompt specifies the desired structure, relevant insights, and any key items that should appear in the summary.

Conversation transcript

Use this mode when you need only a transcript of the conversation, without any summarization or data extraction.

To configure this:

Leave the Variables list empty.
Leave the Post Call Analysis prompt empty.

Note: This mode will generate only the call transcript. The same result can also be achieved by configuring the finish webhook, as described in Post call analysis WebHook section.

Add post call analysis to the agent

After creating the post call analysis in the Post call analysis screen, you must assign it to the specific agent. To do this, use the Post call analysis tab in the Agent Configuration screen.

Defining a post call analysis in the Post Call Analysis screen alone is not enough, you must also assign it to the specific agent in the Agent Configuration screen.

You can add multiple post call analysis instances to the same agent. These instances may operate in different modes, for example, one is may perform structured data extraction while another summarizes the call. Alternatively, they may use the same mode but generate different content, for example, a detailed call summary and an executive summary.

You can also reuse the same post call analysis instances across multiple agents.

Post call analysis WebHook

The Post call analysis WebHook uses the following format:

{
    "account_id": ...,          # account ID
    "conversation_id": ...,     # conversation ID
    "agent": ...,               # agent name
    "post_call_analysis": ...,  # post call analysis name
    "start_time": ...,          # conversation start time
    "end_time": ...,            # conversation end time
    "duration ": ...,           # conversation duration
    "conversation_data": {      # conversation data as populated by Live Hub
        "caller": ...           #   refer to https://techdocs.audiocodes.com/voice-ai-connect >
        "callee": ...           #            Bot integration > Basic behavior > Call initiation
    },                          #   (may be missing for chats)
    "variables ": {},           # conversation variables
    "data": ...                 # extracted post call analysis data
                                #   or plain-text LLM response, if it couldn't be parsed
                                #   or conversation history, if no params are defined
}

The data field is populated based on the selected operation mode:

Structured data extraction mode - contains a dictionary of all extracted parameters.
Call summarization mode - contains the generated conversation summary.
Conversation transcript mode - contains the full conversation transcript.

The status of the WebHook transmission is included in the Post call analysis log entry recorded in the conversation history and displayed in the Logs screen:

response_status – contains HTTP status of the response to WebHook; statuses 2xx indicate successful WebHook transmission; statuses 4xx-5xx indicate error returned by the receiving endpoint; status 700 indicates transmission timeout.
response_text – contains additional information that may help with troubleshooting.

Advanced configuration

You may further customize the Post call analysis behavior via the post_call_analysis advanced configuration parameter:

Parameter	Type	Description
`include_logs`	boolean	Include logs in the Post call analysis transcript.
`join`	boolean	Join data from all Post call analysis instances into a single webhook call. The `post_call_analysis` field will be removed. And the `data` field will contain a dictionary of all post call analysis names and their outputs.
`last_agent`	boolean	Multi-agent topologies use Post call analysis configuration of the main agent, that starts the conversation. Post call analysis configuration of the sub-agents is ignored. You may change this default behavior at by enabling the `last_agent` parameter. When enabled, Post call analysis configuration of the last agent – the one that ends the conversation – will be used.
`conditions`	List[str]	You may specify logical conditions for running Post call analysis. Use variable names and comparison operators, for example: `user_utterance_count > 1` If multiple conditions are specified they are concatenated using AND Boolean operator. Alternatively you may include Boolean operators in your conditions, for example: `sex == "male" and age > 18`