Thank you for reaching out! Based on the information available, you can evaluate your agent's responses programmatically by creating a specialized evaluation agent. This evaluation agent can ask questions, grade responses, and interact with your main agent. Here's a simplified approach: 1. Create an evaluation agent with specific instructions for testing and grading. For example:

class SimpleEvaluationAgent(Agent):
    def __init__(self):
        super().__init__(
            instructions="""
                You are evaluating the performance of a user.
                [evaluation instructions here]
            """,
            stt=deepgram.STT(),
            llm=openai.LLM(),
            tts=openai.TTS(),
            vad=silero.VAD.load()
        )

2. Implement a grading function to assess the responses programmatically: `@function_tool async def grade_answer(self, context: RunContext, result: str, question: str): """ Give a

result

of

PASS

or

FAIL

for each

question

""" self.session.say(f"The grade for the question {question} is {result}") return None, "I've graded the answer."` 3. Configure the evaluation agent to interact with your main agent by updating the `RoomInputOptions`:

await session.start(
    agent=SimpleEvaluationAgent(),
    room=ctx.room,
    room_input_options=RoomInputOptions(
        participant_kinds=[
            rtc.ParticipantKind.PARTICIPANT_KIND_AGENT,
        ]
    ),
)

This method simplifies the process by automating the testing and evaluation of your agent's responses without the need for complex communication protocols or room management. Let me know if you need further assistance! Sources: How to test your agent using another agent