From day 1 to 05 [Agentic Ai Application Challenge @DDS]

Five days into the Building Agentic AI Application Challenge with Decoding Data Science, I’ve taken AgroVn AI from an empty repo to a functional, domain-specific assistant.

Here is what went into Days 1 through 5:

Multi-Agent Core: Orchestrated Groq (Qwen-27B) with LangGraph to retrieve verified crop calendars and extension data.

Lean & Fast: Achieved ~90% accuracy on verified queries with ~1.8s streaming latency at roughly $0.002 per query.

Strict Guardrails: Enforced deterministic safety rules so the agent explicitly states "Not found" instead of guessing crop treatments.

Interface Integration: Connected the backend pipeline to a mobile-friendly Gradio interface with custom table parsing.

Heading into Days 6 to 8 to finalize security checks, optimize runtime, and deploy.

What guardrails do you rely on when deploying AI in high-stakes domains?

Decoding Data Science DDS Business Circle Mohammad Arshad

#AgenticAI #AgroTech #Python #Langchain #LangGraph

#RAG #MultiAgent #Gradio #LLMOps #MachineLearning

#AIBuilders #TechAiCommunity #AIagents #BuildInPublic

#AIChallenge

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From day 1 to 05 [Agentic Ai Application Challenge @DDS]

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