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No interruptions, no repeating information, no cold transfers. The architecture disappears. What remains is the experience.
Two to four seconds of silence after speaking feels broken. The AI processing engine runs inside the media stack, delivering 800-1200ms typical response time. The conversation feels natural.
When a caller interrupts, the AI keeps talking for half a second on bolt-on systems. With media-frame processing, the AI stops within the audio frame. Callers feel heard.
State scattered across webhooks and vendor callbacks disappears on transfer. Platform-native state means context travels with the call. The caller says it once.
A handoff to a human who asks 'how can I help you?' with no context is not a transfer. It is starting over. Warm transfers carry identity, auth state, and a conversation summary.
from signalwire_agents import AgentBase
from signalwire_agents.core.function_result import SwaigFunctionResult
class SupportAgent(AgentBase):
def __init__(self):
super().__init__(name="Support Agent", route="/support")
self.prompt_add_section("Instructions",
body="You are a customer support agent. "
"Greet the caller and resolve their issue.")
self.add_language("English", "en-US", "rime.spore:mistv2")
@AgentBase.tool(name="check_order")
def check_order(self, order_id: str):
"""Check the status of a customer order.
Args:
order_id: The order ID to look up
"""
return SwaigFunctionResult(f"Order {order_id}: shipped, ETA April 2nd")
agent = SupportAgent()
agent.run()
| Platform Capability | What the Caller Feels |
|---|---|
| AI processing engine (800-1200ms latency) | Natural conversation pace |
| Media-frame barge-in detection | AI stops when interrupted |
| Platform-native state management | Never asked to repeat information |
| Step-based conversation flow | Coherent, focused interactions |
| Warm transfer with context | Seamless handoff to humans or other AI |
| Error recovery with structured taxonomy | Transparent handling; caller may not notice |
| Multi-agent orchestration | Complex workflows feel like one conversation |
Pinpoint whether slowness comes from STT, LLM, or TTS. Fix the right layer instead of guessing.
Measure where callers interrupt most often. High barge-in rates at a step may mean the prompt is too verbose.
Identify where callers drop off. Each step is independently measurable because the conversation flow is structured.
Verify that receiving agents (human or AI) get full context. Measure whether transfers preserve the information callers expect.
The AI kernel orchestrates STT, LLM, and TTS from inside the media stack with direct access to the audio stream, eliminating the orchestration overhead of bolt-on pipelines. As low as 600ms with speech-to-speech voice models. The difference between conversational and talking to a machine.
The AI processing engine knows exactly how many milliseconds of audio played and what text the caller approximately heard. When a caller interrupts, the response stops within the audio processing frame.
Caller identity, authentication state, conversation summary, current issue, and actions already taken. The receiving agent (human or AI) has full context.
Yes. A greeting agent identifies the caller, a routing agent determines intent, a specialist handles the request. All within one call, seamless to the caller.
Per-component latency, barge-in frequency, step completion rates, and transfer context quality. Each metric is available through structured event streams.
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Sub-second latency, instant barge-in, and context that never drops.