Latency on the side panel context object workaround is hitting 400ms, rendering the community-referenced fix unusable for our migration timeline. Context object approach just too slow, /api/v2/agent-scripts endpoint rejects dynamic payloads with FLOW_DEFINITION_INVALID, and it’s a regression from Zendesk where {{ticket.custom_field_123}} resolved natively. FLOW_DEFINITION_INVALID
{
"name": "Zendesk Macro Import",
"content": [
{
"type": "text",
"text": "Ticket ref: ${interaction.customAttributes.zd_ticket}\nPriority: ${interaction.customAttributes.zd_priority}"
}
]
}
The /api/v2/agent-scripts endpoint strictly validates against the block schema. Raw interpolation breaks the parser and throws FLOW_DEFINITION_INVALID when the flow tries to render it. Wrap every dynamic field inside a proper text block and use the ${interaction.customAttributes.key} syntax. The parser won’t accept raw Zendesk macro tags. That 400ms delay comes from synchronous calls blocking the render thread anyway. Honestly, the side panel workaround just adds noise. Push the ticket values into customAttributes via a Data Action on flow entry instead. It’ll bypass the context object lag completely. Validate the payload at /api/v2/architect/schemas first. The JSON needs exact block typing or the engine drops it straight to invalid. Keep the request body under 500KB to avoid timeout.
Common mistake here is stuffing template literals directly into the text property. The scripting engine expects discrete variable blocks, not raw string interpolation.
Cause:
The endpoint validator runs a strict schema check against the flow definition. When the parser hits ${interaction.customAttributes.zd_ticket} inside a plain text block, it flags a FLOW_DEFINITION_INVALID error because the runtime doesn’t know how to resolve the path at compile time. The side panel context workaround adds that 400ms lag since it triggers a separate lookup cycle.
Solution:
Switch to the variable block type and map the paths explicitly. The ETL pipeline usually stages the macro exports in S3, runs a quick PySpark transform to swap the Zendesk tokens for GC block definitions, then pushes the cleaned JSON via the bulk endpoint.
{
"name": "Zendesk Macro Import",
"content": [
{ "type": "text", "text": "Ticket ref: " },
{ "type": "variable", "variableName": "zd_ticket", "source": "interaction.customAttributes.zd_ticket" },
{ "type": "text", "text": "\nPriority: " },
{ "type": "variable", "variableName": "zd_priority", "source": "interaction.customAttributes.zd_priority" }
]
}
This keeps the payload under the strict schema limits and drops the latency. The bulk endpoint usually chokes on concurrent PUT requests though. How are you handling the 429 backoffs right now. Gotta run the next Glue trigger.