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Grok Bot+18 agentsVoice AI Agent Builder Skill
Design and implement low-latency voice AI agents with robust interruption handling and state management.
$19.99
Voice AI Agent Builder Skill
Example session with this skill installed
Design a voice agent for a drive-thru menu. It needs to handle noisy environments, support barge-in if the user changes their mind, and call a 'place_order' function safely.
- Read your context and instructions
- Compiled the voice ai agent
The following design outlines a low-latency voice agent architecture for a drive-thru environment, focusing on robust noise handling and idempotent order processing.
Drive-Thru Voice Agent Implementation Plan
| Component | Specification |
|---|---|
| Channel | Outdoor Intercom (Local Audio Processing) |
| Latency Target | < 500ms (Total Round Trip) |
| STT Focus | High-noise robustness, streaming partials |
| TTS Focus | High-volume clarity, low-latency streaming |
| Interruption | Immediate local VAD-triggered playback stop |
Implementation Interfaces
interface SpeechToTextProvider {
startStream(config: AudioConfig): Promise<TranscriptStream>;
stopStream(): void;
onPartialResult(callback: (text: string) => void): void;
}
interface OrderAgentSession {
state: 'LISTENING' | 'THINKING' | 'SPEAKING' | 'INTERRUPTED';
currentOrder: OrderItem[];
cancelPendingActions(): void;
processTurn(input: string): Promise<void>;
}
// Interruption Strategy:
// 1. Local VAD detects speech > threshold.
// 2. Client sends 'STOP' signal to backend via WebSocket.
// 3. Backend cancels LLM generation and clears TTS buffer.
// 4. Session state resets to 'LISTENING' for new turn.
Order Safety & Idempotency
To prevent duplicate charges or items during network jitter, the place_order tool uses a unique transaction_id per session. Retries on the tool call are bounded and check for existing records before execution.
Next steps
- Define the specific noise-cancellation profile for the intercom hardware.
- Select an STT provider with high accuracy for short, food-related utterances.
- Map the state machine transitions for 'add item', 'remove item', and 'confirm total'.
Connects securely to your tools. The creator never sees your data.
About this skill
Building conversational voice agents requires balancing low-latency turn-taking with reliable state management. This skill provides a structured framework for designing and implementing voice AI systems that handle the complexities of speech-to-speech interaction. It moves beyond simple text prompting by addressing the technical realities of audio transport, voice activity detection (VAD), and barge-in handling.
What it does
- Architects voice pipelines by comparing chained STT/LLM/TTS flows against native speech-to-speech models.
- Designs audio handling including codecs, sample rates, noise cancellation, and streaming transcription buffers.
- Manages conversation state with deterministic transitions for listening, thinking, speaking, and interrupted states.
- Implements barge-in logic to stop agent playback and cancel pending generations when user speech is detected.
- Ensures reliability through bounded retries, idempotent tool execution, and graceful recovery from low-confidence transcripts.
How it works
- Define the environment by specifying the channel, expected concurrency, and latency targets.
- Select the stack using provider-agnostic interfaces for speech and model orchestration.
- Map the state machine to handle interruptions, tool calls, and session timeouts.
- Generate implementation artifacts including Mermaid sequence diagrams, interface definitions, and pseudocode.
Frameworks & tools
Works with any STT, TTS, and LLM providers via clean interface patterns. Supports streaming audio protocols (WebSockets, WebRTC), VAD libraries, and telephony integrations.
Why this beats prompting it yourself
Generic prompts often ignore the "physics" of voice, such as audio buffering and race conditions during interruptions. This skill enforces engineering rigor around cancellation, idempotency, and state synchronization that a standard LLM will overlook.
Use cases
- Building low-latency customer support phone agents with telephony integration.
- Designing hands-free mobile assistants that handle noisy environments.
- Developing web-based voice interfaces with real-time barge-in capabilities.
- Prototyping multi-modal agents that require coordinated audio and tool execution.
Known limitations
Does not provide pre-built binary assets or hosting. Requires developers to provide specific SDK documentation for third-party providers to ensure API accuracy.
Requires external SDK documentation for specific provider implementation. Does not include pre-built audio processing binaries or hosting infrastructure.
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- 1
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- 2
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- 3
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