Architect Post Call Data

Set up post-call data collection and evaluation criteria

Two related but separate post-call configs. Data collection answers "what facts were in the call?"; evaluation criteria answer "was the call good?". Both are written with PATCH /v1/convai/agents/$AGENT_ID?branch_id=$BRANCH_ID (host https://api.elevenlabs.io, header xi-api-key: $API_KEY), branch-scoped, live on merge.

Ground the design in real calls first

Before proposing fields or criteria, read the agent and a representative sample of its conversations (in parallel where independent):

• GET /v1/convai/agents/$AGENT_ID?branch_id=$BRANCH_ID: read the system prompt and first_message to see what the agent is positioned to gather, and read the existing platform_settings.evaluation.criteria and conversation_config.platform_settings.data_collection so you extend rather than clobber.
• GET /v1/convai/conversations?agent_id=$AGENT_ID: recent call volume and a sample to inspect.
• GET /v1/convai/conversations/{cid} on 2-3 of those: confirm the target data actually appears in transcripts, and note the exact phrasing the agent uses and where calls terminate early. These payloads carry customer PII; do not copy transcripts into other systems or logs, and respect zero-retention-mode accounts.

Part 1: Data collection (structured extraction)

An independent post-call LLM extraction runs over the transcript for each field. Config lives at conversation_config.platform_settings.data_collection, an object keyed by field name. Each field has type, description (the extraction prompt), an optional enum, and value-source flags.

Design each field against these rules:

• description IS the extraction prompt. Be specific: "The caller's full legal name as confirmed during the call", not "Customer name". Keep it under ~500 characters; long descriptions dilute the instruction.
• Pick the type by the data: boolean for yes/no facts, string with an enum for categories (for example call_outcome with ["completed","declined","transferred"]), integer/number for amounts. Using string for a yes/no fact yields "yes"/"no" text instead of a real boolean.
• Use enum liberally. Even a large enum (50+ values) gives stronger guidance than prose and prevents free-text drift. Cover every realistic outcome; a missing value comes back null.
• Fields are extracted independently, one LLM call each. A field's description cannot reference another field. "Only if coverage_active is yes..." will not work, because this extraction never sees another field's result. Bake any needed condition into this field's own description.
• Scalar values only, no comma-separated multi-value answers. If you need several values, split into a primary enum field plus a secondary free-text string field.
• Design for null. If the caller hangs up early, most fields will be null. That is expected, not an error; tell the user null means "not discussed".

Write it: PATCH /v1/convai/agents/$AGENT_ID?branch_id=$BRANCH_ID with body {"conversation_config":{"platform_settings":{"data_collection":{...}}}}. Merge with the existing fields you read above; do not replace the whole data_collection object unless the user explicitly wants a reset.

Part 2: Evaluation criteria

Each criterion is a separate post-call LLM call scored SUCCESS / FAILURE / UNKNOWN. Its conversation_goal_prompt is capped at 2000 characters. Aim for 3-5 criteria, not an exhaustive list. Write each one against these rules:

• Write as a question, not an instruction. "Evaluate whether the agent collected all required fields", not "The agent should collect all fields". Criteria grade behavior; they do not tell the agent what to do (that belongs in the system prompt).
• Be specific about SUCCESS vs FAILURE, using the exact phrasing you saw in the transcripts: "Mark SUCCESS only if the agent read the disclosure starting with [exact phrase]".
• Always add UNKNOWN guidance for early termination. Every criterion that depends on a later phase needs a line like "If the call ended before reaching this phase, mark UNKNOWN rather than FAILURE." Without it, early hangups drag the success rate down for a step that never ran.
• Group related checks into one criterion. Instead of four disclosure criteria, combine: "Evaluate whether ALL of the following were read: (1)..., (2)..., (3)...". Fewer, richer criteria are cheaper (each is its own LLM call) and easier to read.
• Cover compliance (were required disclosures read?), completeness (were all fields collected?), accuracy (were facts stated correctly?), and boundaries (did the agent stay in scope?).
• Respect the 2000-character limit per criterion. Grouping is how you fit a thorough check; if a grouped criterion is still too long, split along a natural seam rather than trimming the specificity that makes it useful.
• Do not make it brittle. Criteria that fail on valid conversations make the success rate misleading.

Write it: PATCH /v1/convai/agents/$AGENT_ID?branch_id=$BRANCH_ID with body {"platform_settings":{"evaluation":{"criteria":[...]}}}. Send the FULL array; the write replaces the criteria list, so include the criteria you read above plus the new ones.

Verify

Criteria only mean something once conversations are scored against them. Have the user run a simulation test or wait for a few live calls, then re-open graded conversations with GET /v1/convai/conversations/{cid} and confirm the SUCCESS / FAILURE / UNKNOWN verdicts match your judgment. If a criterion fails on a call you would call good, it is too strict; loosen the wording or add the missing UNKNOWN branch.

Related

• If the user wants a value known BEFORE the call (for example an account id passed in at session start), that is a dynamic variable, not a data collection field.
• If fields later come back null or wrong, re-check description specificity, type match, enum coverage, and whether the data is even in the transcript.