Post-call reporting

Every call your agent handles produces structured reporting data automatically. There are two layers, both configured by you in Agent Studio:

• Real-time metrics are written during the call from your flow functions using conv.write_metric.
• Post-call AI extraction runs after the call ends. The full transcript is sent to an LLM via conv.utils.prompt_llm so you can pull out fields that can only be determined by reviewing the whole conversation.

Together they cover the two questions builders typically need to answer for reporting: “What happened at specific moments in the call?” and “What does the call mean once it’s over?”.

Layer 1: Real-time metrics

Inside your flow functions, write metrics at key moments using a single line of code:

conv.write_metric("BOOKING_CONFIRMED")
conv.write_metric("HANDOFF_REASON", "billing_dispute")
conv.write_metric("LANGUAGE", "Spanish")

Each call fires in real time as the conversation progresses and is immediately available in Dashboards, Smart Analyst, and the Conversations API.

Common examples:

You decide what to track. Add conv.write_metric() calls anywhere in your flow functions. See Metrics for naming conventions and how metric definitions affect Smart Analyst.

Layer 2: Post-call AI extraction

After the call ends, the platform can run the full transcript through an LLM to extract structured data that depends on the whole conversation – call reasons, sentiment, summaries, follow-up flags, and so on. You configure this in your End tool (end_function.py).

The pattern has three pieces: a dictionary of extraction queries, a helper function that builds the prompt and calls the LLM, and a call from end_function itself.

EXTRACTION_QUERIES = {
    "CALL_REASON": "Categorize why the customer called (e.g. appointment scheduling, billing dispute, prescription refill).",
    "RESOLUTION": "Was the caller's issue resolved? Answer: yes, no, or partial.",
    "SENTIMENT": "Rate the caller's overall sentiment: positive, neutral, or frustrated.",
    "REPEAT_CONTACT": "Did the caller mention they've called about this before? Answer: yes or no.",
    "ESCALATION_COUNT": "Count how many times the caller asked to speak to a human agent.",
    "AGENT_QUALITY": "Score 0 or 1: 1 = the call was handled well, 0 = repeated misunderstandings or inappropriate responses.",
}

def post_call_extraction(conv):
    # Format the conversation transcript
    conversation_text = "\n".join(
        f"{event.role.capitalize()}: {event.text}"
        for event in conv.history if event.text
    )
    # Build the prompt
    prompt = f"""You are analyzing a completed voice conversation.
## Transcript
{conversation_text}
## Extract the following
{chr(10).join(f'- **{k}**: {v}' for k, v in EXTRACTION_QUERIES.items())}
Return a JSON object with one key per metric."""
    # Call the LLM — returns parsed JSON
    return conv.utils.prompt_llm(prompt, return_json=True, show_history=False)

def end_function(conv):
    # Real-time metrics
    if conv.state.booking_completed:
        conv.write_metric("OUTCOME_BOOKING_COMPLETED")
    # Post-call extraction
    results = conv.functions.post_call_extraction()
    # results = {"CALL_REASON": "appointment scheduling", "RESOLUTION": "yes", ...}
    for key, value in results.items():
        conv.write_metric(key, value)

The extraction runs automatically after every call. You can add or change queries any time by editing the dictionary – no extra configuration beyond shipping your update.

Example queries

You write the extraction instructions in plain English. The LLM does the rest.

When to use each layer

Use real-time metrics when the outcome is known at a specific point in the call – a function succeeded, a booking was confirmed, a handoff happened. Use post-call extraction when the answer depends on the full transcript – sentiment, summaries, categorized reasons, or counts that span the whole conversation.

For each extraction query you plan to expose in dashboards, register a matching metric and write the result back with conv.write_metric. This keeps real-time and post-call data in a single, queryable column per conversation.

Where the data goes

Both real-time metrics and post-call extractions land in the same surfaces:

Once metrics are written, your team can query Smart Analyst directly with questions like:

• “How many callers mentioned insurance concerns this month?”
• “What percentage of calls resulted in a booked appointment?”
• “Show me all calls where follow-up was flagged.”

No separate analytics integration is needed – it’s all native to the platform.

End-to-end flow

Your agent handles a call → real-time metrics fire during the call → the call ends → post-call extraction runs automatically → all data lands in dashboards, Smart Analyst, and the Conversations API.

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