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Ambivert AI has a simple data model for passing information through a call. Understanding it is key to building agents that feel personalised and to extracting useful results after a call.

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The three context objects

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initial_context

Data available to the agent before the call starts — the contact’s name, account details, appointment information, anything the agent should know upfront. It can be set from several places:
  • API trigger — pass it in the request body when calling POST /public/agent/{uuid} or POST /telephony/initiate-call
  • Campaign CSV — columns beyond phone_number automatically become initial_context fields for each contact’s call
  • Pre-call data fetch — enrich the context with data from your CRM or ERP via an HTTP call as the call starts, before the agent speaks
  • Agent Settings — set template context variables on the agent for testing; they’re included in test calls from the workflow editor and ignored on production calls

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Template variables

Values from initial_context are available in your agent’s prompt using {{double_brace}} syntax.
When the call starts, Ambivert AI substitutes the values before sending the prompt to the LLM — so the agent speaks naturally as if it already knows the contact.

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Fallback values

If a variable might be missing or empty, use a pipe (|) to provide a default value:
When customer_name is not set, the agent will say “Hello there” instead of leaving a blank. The syntax is:
If the variable is present and non-empty, the fallback is ignored and the actual value is used.

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Default variables

Built-in variables for current time and weekday, available in any prompt without setting up initial_context. Replace <TIMEZONE> with an IANA timezone name such as Asia/Kolkata, America/New_York, or Europe/London.
When you use a timezone suffix on either current_time or current_weekday, the other variable without a suffix will automatically use the same timezone instead of UTC. For example, if your prompt contains both {{current_time_Asia/Kolkata}} and {{current_weekday}}, the weekday will also be resolved in Asia/Kolkata.

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Telephony variables

For telephony calls (inbound and outbound), Ambivert AI automatically adds these variables to initial_context: For inbound calls, caller_number is the customer’s number and called_number is your Ambivert AI number. For outbound calls, it’s the reverse — caller_number is your Ambivert AI number and called_number is the customer’s number.

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gathered_context

Data the agent collects during the call. You configure what to extract in the agent node’s extraction settings — each variable has a name, type, and a prompt that tells the LLM what to look for. Extracted variables gathered_context is returned in the run record after the call completes and is available in webhook payloads for downstream processing. It is not available as a template variable in Agent prompts — prompts can only reference initial_context fields.

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Data flow example

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Where variables are available