How to build Build an enterprise AI agent

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Build an enterprise AI agent: architecture and implementation guide
Applies to: Noviz AI platform, the open-source ERP AI Agent, ERPNext on Frappe Framework 15 and 16 · Audience: solution architects, ERP developers, technical decision makers · Reading time: about 60 minutes · Last updated 8 October 2026

This guide explains how to design and build an AI agent that works inside an enterprise system of record. It describes the architecture Noviz AI runs in production, the reasons behind each design decision, and the parts you can reuse from our open-source agent. Code samples are short, illustrative excerpts that show how each part is built. They contain no customer business rules and no production prompts.

Key takeaways

The language model reasons; the ERP system decides. Rules, permissions and workflows stay in the ERP.
The agent acts only through typed tools, and every tool call passes one gateway that checks permissions.
Every call runs as the signed-in user, so the ERP's own permissions and audit trail apply unchanged.
Token cost and accuracy are architecture problems: tool filtering, layered prompts and server-side analytics solve them.
A small engine you own is easier to make reliable than a general-purpose agent framework.
Overview
Most organisations already run their operations on a system of record: an ERP, a CRM, an HR suite. An enterprise agent is valuable when it can work with that system the way a trained employee does: read the right records, understand what they mean, prepare transactions, follow the approval process, and explain results. It is risky when it bypasses permissions, invents numbers, or changes data without consent.

The architecture in this guide is designed to keep the value and remove the risk. It has been built and refined on live ERPNext installations, extended to Microsoft Dynamics 365 Business Central, and is published in part as open source so that teams can study and reuse it.

Part What it covers

1. Concepts What distinguishes an enterprise agent from a chatbot, and the principles every design choice follows.
2. Architecture The reference architecture, the two deployment models, and the path of a single request.
3. Components Each building block in detail: plugin, connector, tools, permissions, tool filtering, prompts, context, LLM connection, reasoning loop, rendering, analytics, RAG, scanning and safety.
4. Decisions Why we built our own engine instead of using LangChain or a similar framework, the libraries we chose, and how the system scales.
5. Build it yourself How to install the open-source agent, how the code is organised, and how to extend it.

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