AI Architecture Concept

AI-Native
ERP System

LLMs as the new operating system — with CPU (reasoning), RAM (context), and Filesystem (knowledge). A conceptual architecture for transforming legacy ERP into agentic, intent-driven enterprise software.

Interactive Concept v1.0 · Feb 2026
The New Operating System Metaphor
the new
LLM · OS
Reasoning Engine
⚡
CPU Equivalent
Reasoning Engine
Chain-of-thought, planning, tool selection, decision making. The "processor" that interprets intent and decides actions.
💾
RAM Equivalent
Context Window
Active working memory. Current conversation, retrieved documents, tool results, user identity. Limited but fast.
🗄️
Filesystem Equivalent
Knowledge Layer
Vector DB (RAG), MCP servers, structured data. Persistent storage retrieved on demand. ERP schema, docs, policies.
🧠
Long-term Memory
Episodic Store
Customer history, past decisions, learned patterns. What this agent knows about this organization over time.
🛡️
Kernel / OS Equivalent
Policy & Guardrails Engine
Access control, compliance rules, audit logging. What the agent is permitted to do — enforced at the OS level, not the app level.
Click any node to explore
The LLM is not just a feature added to software — it becomes the operating system through which all business logic runs. Each component of the traditional OS has an AI-native equivalent that transforms how software is designed, built, and run.
AI-Native ERP Component Architecture
Input Layer
Intent Interface
Natural language, chat UI, voice. Intent expressed, not menu-navigated.
MCP Layer
MCP Tool Servers
Business capabilities exposed as MCP tools. Not DB tables — semantic business operations.
Data Layer
Semantic Layer + RAG
Schema + business concepts translated. Vector DB for docs, structured API for transactions.
Central Intelligence
Agent Runtime
Multi-step reasoning · Tool orchestration · Memory management
Plan
Retrieve
Execute
Verify
Respond
LLM Options
Cloud Hosted
GPT-4 / Claude
Local Llama
Process Layer
UAPF / BPMN Engine
Machine-readable processes. Business rules as code, not PDF documentation.
Memory Layer
4-Layer Memory
Working · Episodic · Semantic · Procedural. Organizational intelligence persisted across sessions.
Governance
Policy Engine + Audit
What agents are permitted to do. Full audit trail of reasoning for EU AI Act compliance.
Click any component to explore
Each component has a specific role in the AI-native ERP stack. Unlike traditional ERP where business logic is embedded in application code, the AI-native stack separates knowledge, process, reasoning, and execution into distinct, replaceable layers.
Traditional ERP vs AI-Native: Same Task, Different Paradigm
Scenario: "Prepare and send overdue invoice reminders for Q4 customers, prioritized by relationship risk"
Traditional ERP (Legacy)
01
Navigate to AR module
User opens Accounts Receivable → Reports → Aging Report
Manual Navigation
02
Filter and export
Set date ranges, select overdue filter, export to Excel
Data Extraction
03
Manual prioritization
User manually sorts, cross-references CRM notes, uses judgment to prioritize
Human Synthesis
04
Template selection
Choose from fixed email templates, manually customize per customer
Template-Driven
05
Send and log manually
Send emails, manually log sent date in ERP, repeat for each customer
Repetitive Labor
06
No learning
Process outcome not captured. No improvement next cycle. Same effort repeated quarterly.
Zero Feedback Loop
→
AI-Native ERP (Agent)
01
Intent expressed
User types: "Handle Q4 overdue reminders, prioritize by relationship risk"
Natural Language
02
Agent plans execution
Agent decomposes task: retrieve overdue list → assess relationship risk → draft messages → confirm → send
Autonomous Planning
03
Multi-source retrieval via MCP
Calls AR MCP tool + CRM memory + payment history + industry risk signals simultaneously
Parallel Retrieval
04
Context-aware drafting
Drafts personalized message per customer based on relationship history, amount, and risk profile
Personalized at Scale
05
Human confirms, agent executes
User reviews summary + exceptions. Approves. Agent sends, logs, updates records automatically.
Human-in-loop
06
Outcome captured → learning
Response rates, payment outcomes stored in episodic memory. Next cycle is better. System improves.
Continuous Learning
Live Request Flow Through AI-Native ERP Stack
USER Intent Input AGENT RUNTIME LLM Reasoning Plan → Tools → Verify MCP LAYER Tool Server GOVERNANCE Policy Engine Permit · Deny · Audit KNOWLEDGE RAG / Vector DB TRANSACTIONS ERP Database PROCESS UAPF Engine MEMORY Episodic Store LLM MODEL Foundation Model Inhouse-tuned / API OUTPUT Response + Action Result · Audit · Learn — SEMANTIC + DATA LAYER — Local Llama On-Premise Claud Hosted Recommended GPT-4 / Claude API Fallback MODEL ROUTING STRATEGY
→ Invoice Query Flow
User intent
→
Agent plans tool calls
→
Policy check
→
MCP retrieves invoice + customer data
→
LLM synthesizes
→
Response + audit log
Implementation Strategy — ERP Transformation
Phase 01
Foundation
Months 1–6 · No model training
Build semantic layer over Horizon DB schema
Deploy MCP sidecar server per customer (Docker, read-only)
Connect GPT-4 / Claude API as reasoning engine
Instrument all agent actions for audit logging
Document failure modes — where does the LLM underperform?
Phase 02
Domain Tuning
Months 6–18 · Fine-tuning begins
Fine-tune Llama 3.3 70B on anonymized Horizon transaction patterns
Per-vertical LoRA adapters: accounting, payroll, logistics
Latvian business language continued pre-training
Synthetic training data generation from UAPF process definitions
Deploy Cloud-hosted model, replace API calls for core tasks
Phase 03
Moat Building
Year 2+ · Defensible AI asset
Global model trained on aggregate anonymized data from all customers
Shared agent runtime
Cross-product memory: HR + payroll + accounting share context
Capability registry: products register MCP tools, agents discover them
Network effect: more customers → better model → better product
LLM Model Selection Guide
Model Deployment Latvian? Cost Best For Verdict
Llama 3.3 70B On-premise / Claud-hosted Partial Hardware CAPEX Sensitive data, base for fine-tuning ✓ Start here for tuning
Mistral Large API / Self-hosted Partial Medium API cost European data residency, structured tasks ✓ Good EU option
GPT-4o / Claude Sonnet API only Good High per-token Complex reasoning, Latvian language Start / Fallback only
Llama 3.2 3B Customer-site local Poor Near zero Structured queries, classification only Narrow tasks only
Fine-tuned Claud-hosted Trained in Shared infrastructure All ERP tasks for customers ✓✓ Phase 3 target
Per-client model Per customer — Prohibitive Nothing at SME scale ✗ Not viable
KEY ARCHITECTURAL PRINCIPLE
The model provides reasoning capability. The MCP server provides data access. The UAPF engine provides process logic. Fine-tuning improves domain fluency. RAG provides current knowledge. No single layer does everything — the architecture is the moat, not any individual model.