Building a Voice-Controlled AI Agent using AssemblyAI and Groq

Introduction

In this project, I built a voice-controlled AI agent that converts spoken commands into executable actions like generating code and creating files.

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Architecture

The system follows a modular pipeline:

Audio → STT → Intent Detection → Tool Execution → Output

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Technologies Used

• AssemblyAI for speech-to-text
• Groq LLM (llama-3.1-8b-instant) for intent classification
• Streamlit for UI
• Python for backend agent logic

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How it Works

1. User uploads audio
2. Audio is transcribed into text
3. LLM detects intent (multi-intent supported)
4. Agent executes actions
5. Output is displayed and files are created

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Challenges Faced

• Ollama instability on local setup
• Model deprecations in Groq
• Handling multi-intent parsing
• Debugging silent failures in Streamlit

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Key Learnings

• Importance of fallback mechanisms
• API-based models are more stable than local inference
• Proper debugging is critical in agent systems

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Future Work

• Add real-time voice input
• Integrate memory and context
• Add RAG for knowledge-based queries

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Conclusion

This project demonstrates how AI agents can combine speech, reasoning, and actions into a seamless user experience.

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