Improve evaluators using AI by providing natural language feedback.
How it works:
apply_changes=trueResponse: Returns progress_id for polling status at /scenario-agent-progress/
Multi-turn improvements: Use the context field to maintain conversation history across requests.
API Key Authentication. It should be included in the header of each request.
Improve evaluators using AI by providing natural language feedback.
Provide evaluator IDs and your improvement request, and the AI will suggest changes.
Your improvement request (e.g., 'Make instructions more detailed')
Agent ID - optional, automatically inferred from evaluators if not provided
Project ID - optional, automatically inferred from evaluators if not provided
Session ID for tracking - automatically created if not provided
Conversation history for multi-turn improvements. Allows iterative refinement without sessions.
Example:
[
{
"role": "user",
"content": "Add DTMF tool to all evaluators"
},
{
"role": "assistant",
"content": "Added DTMF tool to 3 evaluators"
},
{
"role": "user",
"content": "Make the instructions more detailed"
},
{
"role": "assistant",
"content": "Updated instructions to be more detailed for 3 evaluators"
}
]Number of scenarios to generate in clarify mode
x >= 1Knowledge Base file IDs for additional context in clarify mode
Evaluator IDs to improve. Provide as list [1, 2, 3] or string 'all'
Set to true to apply the AI-suggested changes to your evaluators
AI-suggested changes from previous response - use with apply_changes=true to apply them
User's response when agent clarification was needed
Evaluator IDs that required agent selection
Tool calls held from a previous agent-clarification response