Agent vs RAG chatbot?

RAG answers from docs. Agent also executes system actions.

For multi-step yes. Simple tool loop can be lighter.

RBAC on tools, confirm for critical actions, full audit log.

We use for tool standardization. See MCP service page.

STT/TTS + agent backend — Voice AI service.

From €5,000 depending on tools and channels.

Versioned prompts, eval CI, optional tuning SLA.

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AI Agents

AI agent development

Agents that act: call CRM, generate documents, escalate to humans — not just chat.

Message on Telegram

An AI agent is an LLM that invokes tools: create lead, fetch catalog price, generate PDF, query 1C stock. Bober AI Systems builds agents for sales, support and internal ops.

Yandex Telemost Agent case: voice/chat assistant with integrations. ELIA/KP-LLM sales agent: SKU pick and proposal assembly. Shared architecture: LangGraph + tools + guardrails.

MCP standardizes agent access to CRM, files and APIs — less custom glue, easier audit. We deploy MCP servers in private contour.

Human-in-the-loop mandatory for financial and legal actions: agent drafts → human confirms → commit.

Production 4–8 weeks. Eval on happy path and edge cases. Budget from €5,000.

We sell measurable automation of concrete steps — not autonomous AGI hype.

Simple bot limits

—Chatbot replies but does not create CRM deals
—Multi-step tasks need manual copy between systems
—No context memory across sessions and channels
—Agent errors not logged or rolled back

Deliverables

—Agent with tool registry: CRM, search, documents, calculators
—LangGraph state machine with human-in-the-loop
—MCP servers for secure system access
—Monitoring, eval and runbook

How we build agents

Tool design

Minimal API surface, idempotent writes, clear LLM error messages.

Graph workflow

Nodes: plan → act → verify → escalate. Checkpoints for resume.

Safety

Tool allowlist, PII filters, spend limits, audit trail.

Eval

Scenario tests, tool call assertions, regression suite.

Agent architecture

—LangGraph orchestrator + PostgreSQL state
—Tool layer: MCP servers / REST to CRM, 1C, RAG
—LLM: private GigaChat or self-hosted
—Channels: Telegram, web, CRM sidebar
—Observability: LangSmith / custom traces

Impact

−45%

manual steps in multi-step scenarios

4–8 wks

production agent

24/7

handling routine requests

Related case studies

Yandex Telemost Agent — ИИ для встреч и продаж

Автоматические итоги встреч в CRM

1С · Bitrix · YandexGPT · JavaScript

Команда экономит время, быстрее обрабатывает итоги встреч, не забывает договорённости и повышает качество сопровождения клиентов и продаж.

300 000 ₽

View case study →

Автоматизация коммерческих предложений

45 мин → 2 мин на КП

MySQL · CRM · Python · LLM

Один диалог вместо ручного прайса и Word. Цены и артикулы только из каталога — без выдуманных позиций. Таблица, НДС, условия и скачивание DOCX/PDF.

300 000 ₽

View case study →

FAQ

Agent vs RAG chatbot?: RAG answers from docs. Agent also executes system actions.
LangGraph required?: For multi-step yes. Simple tool loop can be lighter.
CRM write safety?: RBAC on tools, confirm for critical actions, full audit log.
MCP?: We use for tool standardization. See MCP service page.
Voice?: STT/TTS + agent backend — Voice AI service.
Budget?: From €5,000 depending on tools and channels.
Prompt support?: Versioned prompts, eval CI, optional tuning SLA.

Related services

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Agents that act: call CRM, generate documents, escalate to humans — not just chat.