---
title: "Discussion on \"Start Deterministic: Design Principles for Routers and Orchestrators in Multi-Agent Systems\" | Hashnode"
description: "Discussion on \"Start Deterministic: Design Principles for Routers and Orchestrators in Multi-Agent Systems\". I learned this lesson the practical way: by building agentic systems, watching them work, watching them fail, and then slowly realizing that “more agents” is not the same thing as “more intelligence.”"
url: "https://hashnode.com/posts/start-deterministic-design-principles-for-routers-and-orchestrators-in-multi-agent-systems/6a7bd956d7f777cb689cf21f"
---

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# [Start Deterministic: Design Principles for Routers and Orchestrators in Multi-Agent Systems](https://tokenbytoken.hashnode.dev/start-deterministic-design-principles-for-routers-and-orchestrators-in-multi-agent-systems?utm_source=hashnode&utm_medium=feed)

I learned this lesson the practical way: by building agentic systems, watching them work, watching them fail, and then slowly realizing that “more agents” is not the same thing as “more intelligence.”

Aug 12 · 10 min read · tokenbytoken.hashnode.dev  
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## Written by

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

@skantar1

  
[View profile](https://hashnode.com/@skantar1)

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!

## Responses 2

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[Ahmet Özel](https://hashnode.com/@ahmetozel)

[Sep 21](https://hashnode.com/posts/start-deterministic-design-principles-for-routers-and-orchestrators-in-multi-agent-systems/6a7bd956d7f777cb689cf21f/comment/6ab0cad6cae72d020229b0a7)

Start deterministic is the advice I would give too, and the reason is debuggability rather than accuracy. A deterministic router that is wrong 10% of the time is wrong in ways you can enumerate and fix; an LLM router wrong 5% of the time is wrong differently each run, so you never accumulate knowledge about the failure.

From the document-AI side there is a free benefit too: a rule-based router produces labelled data as a byproduct, so when you do want a learned router later you have a training set drawn from real traffic rather than guesses.

Worth stating the reverse condition as well. The honest trigger for going non-deterministic is when the rule set stops being enumerable, not when someone wants it to feel smarter.

[Kartik N V J K](https://hashnode.com/@kartik-nvj)

[Aug 21](https://hashnode.com/posts/start-deterministic-design-principles-for-routers-and-orchestrators-in-multi-agent-systems/6a7bd956d7f777cb689cf21f/comment/6a88a2185f4e7d6150a533eb)

Starting deterministic is advice I wish I had taken sooner: a rules-based router is far easier to debug than a model deciding every hop. I only reach for an LLM router when the branching genuinely cannot be enumerated. Where did you draw the line between deterministic routing and letting the model decide?