# I Built a Voice AI Assistant for Indian Farmers — My 10-Day Voice Agent Journey > Source: > Published: 2026-08-15 05:59:16+00:00 What if a farmer could simply pick up a phone, speak naturally in Malayalam or English, and ask: “*What is today's market price?*” or “*It's going to rain tomorrow. Should I spray my crops today?*” or even: “*My crop is getting damaged. Can someone help me?*” No typing. No complicated menus. Just a conversation. That was the idea behind Farm & Field, the voice agent I built during the 10 Days of Voice Agents — **VoiceForBharat** Edition challenge by **Murf AI**. Over these 10 days, I went from building a basic voice conversation to creating an agricultural voice assistant with memory, real-time tools, outbound calling, human escalation, call analytics, multilingual conversations, and a specialist agent. I built Farm & Field, a voice assistant designed to help farmers with everyday agricultural questions. Farm & Field can: The AI should not try to do everything itself. If it needs current information, it uses a tool. If the problem needs specialized knowledge, it hands the conversation to a specialist. If the situation is beyond what the AI can safely handle, it asks a human for help. A voice agent is basically four main components connected together: **Speech-to-Text (STT)** — converts the farmer's speech into text. I used Deepgram. **LLM** — understands the conversation and decides what to do. I used Google Gemini. **Text-to-Speech (TTS)** — converts the response back into voice. I used Murf Falcon. **Real-time transport** — carries the audio between the user and agent. I used LiveKit. On top of this, Farm & Field has: **🇮🇳 Malayalam, English and Manglish support** Farmers don't always speak in one language. For example: "*Ente rubber crop-il leaves yellow aakunnundu.*" The agent can recognize Malayalam/Manglish conversations and adapt the TTS voice accordingly. This made the conversation feel more natural for Indian users. **Murf Falcon for fast voice responses** I used Murf Falcon for TTS because latency is very important in voice conversations. A slow response makes a voice agent feel like a chatbot that is reading messages aloud. With a faster TTS response, the conversation feels much more natural. **Memory with consent** Farm & Field can remember useful information about returning farmers, such as: But the agent asks for permission before saving information. This was important because memory should be useful without automatically storing everything a user says. **Tools instead of guessing** The agent doesn't rely on the LLM to guess live information. For example, when a farmer asks: "What is today's rubber price?" the agent uses the market-price tool. The same approach is used for weather information. This makes the system more reliable. **Human escalation** The agent can recognize situations where AI should not be the final answer. I implemented escalation for cases such as: The farmer gives consent before a human-help request is created. **Specialist agent** I created a separate Crop Problem Specialist for crop disease and pest-related conversations. The main agent can hand the conversation to the specialist without making the farmer repeat everything. The specialist also has a different voice, making the handoff clear to the user. **Outbound calls** Farm & Field can initiate calls for specific alerts, such as: I used LiveKit SIP for the outbound calling workflow. **Call analytics** The system also tracks call information such as: This helped me understand what happened during conversations instead of treating every call as an isolated interaction. **Repository**: [https://github.com/safdarsidhik/murf-livekit-starter/tree/main](https://github.com/rohitkumar31/murf-livekit-starter) Built with **Murf Falcon** — the fastest TTS API I used across this build Part of 10 Days of Voice Agents — **VoiceForBharat Edition by Murf AI** If you're building your own voice agent for an underserved use case, happy to answer questions — drop a comment.