---
title: "Discussion on \"The Agent Domain Control Plane: A Reference Architecture for Production Multi-Agent Systems\" | Hashnode"
description: "Discussion on \"The Agent Domain Control Plane: A Reference Architecture for Production Multi-Agent Systems\". Most agent architecture diagrams are too simple.\nThey usually show something like this:\nUser -> Agent -> Tools -> Answer\n\nOr, if the system is more advanced:\nUser -> Router -> Orchestrator -> Speciali"
url: "https://hashnode.com/posts/the-agent-domain-control-plane-a-reference-architecture-for-production-multi-agent-systems/6a9625d6c2f2e96edf50c64b"
---

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# [The Agent Domain Control Plane: A Reference Architecture for Production Multi-Agent Systems](https://tokenbytoken.hashnode.dev/the-agent-domain-control-plane-a-reference-architecture-for-production-multi-agent-systems?utm_source=hashnode&utm_medium=feed)

Most agent architecture diagrams are too simple. They usually show something like this: User -> Agent -> Tools -> Answer Or, if the system is more advanced: User -> Router -> Orchestrator -> Speciali

Sep 1 · 17 min read · tokenbytoken.hashnode.dev  
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## Written by

[Sai Sandeep Kantareddy](https://hashnode.com/@skantar1)

@skantar1

  
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Engineer

Senior ML Engineer | GenAI + RAG Systems | Fine-tuning | MLOps | Conversational & Document AI Building reliable, real-time AI systems across high-impact domains — from Conversational AI and Document Intelligence to Healthcare, Retail, and Compliance. At 7-Eleven, I lead GenAI initiatives involving LLM fine-tuning (Mistral, QLoRA, Unsloth), hybrid RAG pipelines, and multimodal agent-based bots. Domains I specialize in: Conversational AI (Teams + Claude bots, product QA agents) Document AI (OCR + RAG, contract Q&A, layout parsing) Retail & CPG (vendor mapping, shelf audits, promotion lift) Healthcare AI (clinical retrieval, Mayo Clinic work) MLOps & Infra (Databricks, MLflow, vector DBs, CI/CD) Multimodal Vision+LLM (part lookup from images) I work at the intersection of LLM performance, retrieval relevance, and scalable deployment — making AI not just smart, but production-ready. Let’s connect if you’re exploring RAG architectures, chatbot infra, or fine-tuning strategy!

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