DLE/docs.en/back-docs/multi-agent-architecture.md

English | Русский

Multi-Agent AI Architecture in DLE

Concept: Separate specialized agents for different tasks, using one local Ollama model with different system prompts, rules, and interfaces.

---

Table of Contents

1. Architecture concept
2. Agent types
3. System architecture
4. Agent configuration
5. Agent interfaces
6. Knowledge base
7. Agent workflow

---

Architecture concept

Principles

1. One model, many agents — all use one Ollama instance (e.g. qwen2.5:7b); differentiation by prompts and rules.
2. Isolation — each agent has its own prompt, rules, RAG tables, channels, route, permissions.
3. Task specialization — support (user Q&A), content editor (posts, articles), others (analyst, translator, procurement).
4. Separate interfaces — each agent has its own UI and access path.

---

Agent types

1. Support agent

Role: Answer user messages. Uses RAG (FAQ, docs), strict mode (minimal generation), system prompt “professional support assistant”. Interface: chat (web, Telegram, email). Knowledge: FAQ, product docs, client knowledge base.

2. Content editor agent

Role: Create content on request. RAG: platform instructions, company style, examples. Creative mode. System prompt “content marketer and editor”. Interface: /content-editor page. Knowledge: platform instructions, style, examples, keywords, CTAs.

3. Other possible agents

Analyst (reports, trends), Translator (localization), Procurement (suppliers, terms).

---

System architecture

Single Ollama model → multiple agents (Support, Content editor, Others), each with own prompt, rules, RAG, interface.

Storage: table ai_agents — id, name, role, description, system_prompt_encrypted, rules_id, selected_rag_tables, enabled_channels, interface_route, permissions_required, is_active. Links to ai_assistant_rules and RAG tables.

---

Agent configuration

Steps: (1) Basic info — name, role, description. (2) System prompt. (3) Rules (from or new). (4) RAG tables. (5) Interface — route, permissions, channels. (6) Activate and test.

Example: Support — strict (temperature 0.3, searchRagFirst, no generateIfNoRag), FAQ + docs, chat. Content editor — creative (0.7, generateIfNoRag), instructions + style + examples, /content-editor, web only.

---

Agent interfaces

Support: embedded in main chat (HomeView); auto on message; expand/collapse; history.

Content editor: page /content-editor — request field, content type, platform, generate → edit → save/export, history. Editor role only.

---

Knowledge base

Support: FAQ, Documentation, Client knowledge base. Content editor: Platform instructions, Company style, Content examples, Keywords, CTAs. RAG search → context → LLM; each agent only sees its own tables.

---

Agent workflow

Support: message → RAG search (FAQ, docs) → if found use it, else suggest operator → send reply.

Content editor: request + type + platform → RAG (instructions, style, examples, keywords) → generate content → show in UI → edit/save/export.

---

Advantages

Specialization, flexibility, isolation, scalability (one model, many agents), clear responsibility.

---

Comparison: single vs multiple agents

	Single agent	Multiple agents
Specialization	General, less precise	Per-task, more precise
Configuration	One set for all	Per task
Knowledge base	Shared	Isolated per agent
Interface	One	Per agent
Flexibility	Harder to adapt	Easy to add agents

---

Next steps

1. Create ai_agents table
2. Agent management service
3. Adapt AI Assistant for multiple agents
4. Content editor UI
5. Support agent (adapt existing)
6. Content editor knowledge base
7. Test both agents

---

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

Last updated: January 2026