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DLE AI Agents — Building Specialized Business Agents

Concept: one local model — many specialized agents. Each agent is tailored to a specific business process: its own prompt, rules, knowledge base, and interface.

Table of Contents

  1. What and Why
  2. Architecture
  3. How to Create an Agent
  4. Agent Examples
  5. Technology Stack
  6. Advantages Over Cloud Solutions
  7. Economic Impact

What and Why

DLE provides tools to create AI agents — specialized assistants, each responsible for a distinct business process.

This is not one generic chatbot. It is a builder where you:

  • Create an agent for a specific task (support, content, procurement, analytics)
  • Set its role via system prompt
  • Attach a knowledge base (RAG tables) with relevant data
  • Configure behavior rules (strict, creative, hybrid)
  • Bind to channels (web chat, Telegram, Email)
  • Get an isolated specialist working 24/7

All agents use one local Ollama model on your server. They differ by system prompts, rules, and connected data. Data never leaves your server.

Architecture

Principle: One Model — Many Agents

One Ollama instance (e.g. qwen2.5:7b) serves multiple agents. Each has its own prompt, rules, RAG tables, channels, and UI. Agents are isolated and do not affect each other.

Request Flow

User request → agent determined by channel/route → agent config (prompt, rules, RAG) → query vectorization (Ollama mxbai-embed-large) → RAG search (FAISS) → LLM response with RAG context + system prompt + history → optional cache (TTL 1 h) → response to user.

How to Create an Agent

Step 1. Basic Info

  • Name — e.g. “Support Agent”, “Content Editor”, “AI Procurement”
  • Role — support, content_editor, analyst, purchaser, etc.
  • Description — what the agent is for

Step 2. System Prompt

Defines identity and behavior. Examples:

Support: “You are a professional customer support assistant. Answer only from the knowledge base. If no answer — suggest contacting an operator. Do not invent prices, terms, or conditions.”

Content editor: “You are a professional content marketer and editor. Use company style from the knowledge base. Follow platform guidelines. Use keywords and hashtags from the base.”

Step 3. Rules (JSON)

{
  "searchRagFirst": true,
  "generateIfNoRag": false,
  "checkUserTags": true,
  "temperature": 0.3,
  "maxTokens": 500
}
Parameter Effect Support Content Analytics
temperature Creativity (0.0–1.0) 0.3 0.7 0.2
searchRagFirst Search knowledge base first true true true
generateIfNoRag Generate if not in base false true false
maxTokens Max response length 500 2000 1000

Step 4. Knowledge Base (RAG Tables)

Attach spreadsheets the agent will search: Support → FAQ, product docs; Content → platform instructions, style, examples, keywords; Procurement → supplier base, terms, prices. Tables need columns designated as “Question for AI” and “Answer for AI” for vector indexing.

Step 5. Channels and Interface

Channels: web chat, Telegram, Email, SMS. Route: e.g. /content-editor. Set which roles can access.

Step 6. Activate

Enable the agent; it starts handling requests on the selected channels.

Agent Examples

1. Customer Support Agent

Task: answer customer questions 24/7 from the knowledge base. Strict mode (only from base), temperature 0.3, RAG: FAQ, docs. Channels: web chat, Telegram, Email. If no answer → suggest operator.

2. Content Editor Agent

Task: create social posts, blog articles, emails in company style. Creative mode, temperature 0.7, RAG: platform instructions, style, examples, keywords, CTAs. Interface: /content-editor, Editor role.

3. AI Procurement Agent

Task: help choose suppliers and compare terms. Hybrid mode, temperature 0.5, RAG: supplier base, terms and prices. Example: “Who supplies electronics with delivery up to 3 days?” → top 3 from table with filters.

4. Other Possible Agents

Analyst (reports, trends), HR assistant (screening, policies), Translator (glossaries, style), Legal assistant (contracts, norms). Each = new combination of prompt, rules, and tables.

Technology Stack

Component Technology Purpose
LLM Ollama (qwen2.5:7b or other) Generation, dialogue
Embedding mxbai-embed-large Text vectorization
Vector DB FAISS Semantic search
Main DB PostgreSQL Agents, knowledge, history
Cache Node.js Map + TTL Fast repeat queries (< 50ms)
Queue AI Queue Priority processing
Encryption AES-256 Prompts and settings encrypted

RAG Search Methods

Semantic (FAISS), keyword, hybrid (e.g. 70% semantic, 30% keyword). Optional: fuzzy search, stemming, keyword extraction.

Advantages Over Cloud Solutions

DLE (local) ChatGPT API Claude API
Cost $0 ~$0.02/request ~$0.03/request
Confidentiality 100% on your server Data at OpenAI Data at Anthropic
Speed (cached) < 50ms 500–2000ms 500–2000ms
Offline Yes No No
Business tuning Full: prompts, rules, RAG Limited Limited
API limits None Yes Yes
Number of agents Unlimited Separate API per use Separate API per use

Economic Impact

API Cost Savings

Requests/month ChatGPT API Claude API DLE
10,000 $2,400/year $3,600/year $0
50,000 $12,000/year $18,000/year $0
100,000 $24,000/year $36,000/year $0

Process Automation Savings

Each agent replaces routine work. Example for a mid-size company: support agent ($57,600), procurement ($64,800), HR ($57,600), content ($86,400), analyst ($144,000), partners ($43,200), training ($30,000), API savings ($24,000–36,000) → total about $507,600–519,600/year. DLE license: $1,000 one-time.

5-Year Comparison with SaaS

Typical SaaS stack (CRM, chatbot, email, AI API): ~$39,000 over 5 years. DLE: $1,000 + $0 AI + free updates 5 years → savings about $38,000.

Additional Materials

Support

© 2024-2025 Alexander Viktorovich Tarabanov. All rights reserved.

Last updated: February 2026