Discuss an agent

AgentZone — AI agentsTasks · Tools · Actions · Control

Not chatbots. Digital employees connected to your systems.

We build AI agents that work inside your business processes: they take a task, use your company’s data and tools, act and return the result — with approval wherever it is needed.

Show what can be automatedDiscuss an AI agent
Agent run WAITING APPROVALDemo Scenario

TASK

Prepare the weekly sales report and send the main deviations to the head of sales.

  1. 00:00.0task.received goal: weekly sales report → head of sales
  2. 00:00.4connect CRMscope: deals, read-only
  3. 00:01.1fetch_sales(week=38) CRM1 284 deals · 4 regions
  4. 00:01.6connect Databasedwh.sales_daily, read-only
  5. 00:02.9compare(week, plan) Database3 deviations above 10%
  6. 00:04.1draft_summary 5 bullet points · sources linked
  7. 00:04.7create_report Filessales_week_38.pdf
  8. 00:05.2request_approval Emailwaiting: head of sales
  • CRM
  • Email
  • Calendar
  • Database
  • Files
  • ERP
  • Telegram
  • API
  • Web

The report is ready. Send it to the head of sales?

02Playground

What an agent can do

Pick a department and a task. On the left the task, in the centre the actions and their statuses, on the right the tools the agent uses. We show actions, not the model’s “thoughts”.

TASK

Demo scenario: this site has no access to your data.

ACTIONS

  1. readingCRM connected · 148 lost deals loaded (90 days)
  2. preparingloss reasons grouped · 6 groups
  3. searchinglast client emails matched to 97 deals
  4. preparingmain pattern detected: price after proposal
  5. sendingreport created · lost_deals_q3.pdf

The main loss reason is price after the proposal. A report with example deals is ready.

TOOLS

  • CRM
  • Email
  • Calendar
  • Database
  • Files
  • ERP
  • Telegram
  • API
  • Web
03Agent types

The agents we build

  1. 01answers requests
  2. 02knows the knowledge base
  3. 03creates tickets
  4. 04checks order status
  5. 05hands over to a person
Learn more
04Agent or bot

How an agent differs from a bot

BotZone · scenario

  1. Command
  2. Scenario
  3. Reply / action

A predefined scenario: fast, predictable and cheap. Ideal when the options are few.

AgentZone · goal

  1. Goal
  2. Context
  3. Plan
  4. Tool choice
  5. Actions
  6. Result check
  7. Approval / done

The agent picks tools for the goal and checks the result. Needed when a task does not fit a scenario.

Need a regular scripted Telegram bot?

Telegram botsBotZone
05Architecture

How an AI agent is built

The model is only one part of the system. Around it are the knowledge base, tools, memory and the limits that separate a deployment from a demo chatbot.

Guardrails

permissions
the agent sees only what its role allows
approval
critical actions only after a person approves
rbac
data access by employee role
logging
every tool call is logged
audit trail
who did what and when can be reconstructed
rate limits
limits on actions and spend
tool restrictions
an allow-list of tools and parameters
More about security
06Human control

We automate the work; critical actions stay under human control

The agent prepared a payment and checked the documents. From here you decide, as the manager. Without approval the payment does not go out.

Payment

Demo Scenario
Payee
Supplier LLC
Invoice
INV-2291
Amount
18,400,000 UZS
  1. Prepared
  2. Waiting approval
  3. Approved
  4. Executed

You are the finance manager. Approve the payment?

AUDIT LOG

  1. agentpayment.prepared INV-2291 · checks: invoice ↔ contract ↔ delivery ✓
  2. agentapproval.requested → finance manager
07Builder

Sketch your AI agent’s architecture

Pick tasks, sources, actions and the autonomy level — the diagram appears on the right: employee, agent, data, approval and actions.

01 · Tasks
02 · Data sources
03 · Actions
04 · Autonomy
Preliminary architectureNodes: 9
AGENT
Reporting agent

CONTEXT
CRM · Database · DWH · Email

ACTIONS
Send email

CONTROL
Every action approved by a person · Role-based access to sources · Audit log of every tool call

This is a preliminary outline. The architecture may change once we review the process and the data.

08Foundation

An agent is only as good as its data

If your data lives in dozens of spreadsheets and disconnected systems, we build the data layer first — otherwise the agent will be confidently wrong.

Is your data spread across spreadsheets and disconnected systems?

Prepare the dataDataZone

AI has to fit your existing infrastructure

When the agent needs new APIs, portals, middleware or internal services, the DevZone team joins in.

Does the agent need new APIs, portals or middleware?

Integration developmentDevZone
09Technology

Technology by category

Models
OpenAI · Anthropic · Google · Open-source models · Private models
Runtime
Tool calling · MCP · Workflow engines · Queues · Background jobs
Data
Vector search · PostgreSQL · Object storage · Enterprise search
Integrations
CRM · ERP · Google Workspace · Telegram · Email · Custom API
10Security

AI agent security

For enterprise clients this is the first question. Here is what we build into every agent.

  • RBAC

    The agent acts with the user’s or its own role’s rights — never wider.

  • Audit logs

    Requests, tool calls and decisions are logged and available for review.

  • PII handling

    Personal data is masked wherever the model does not need it.

  • Access isolation

    Data of different clients and departments never mixes in one context.

  • Prompt injection

    Text from emails and documents is data, not instructions for the agent.

  • Tool boundaries

    Each tool is limited in actions, parameters and quotas.

  • Approval workflows

    Payments, outbound emails and data changes go through approval.

  • Model fallback

    If a model is unavailable, the process falls back to another one or to a person.

  • Data retention

    Retention of conversations and logs follows the company’s policy.

  • Secrets

    Keys and tokens live in a secrets store, never in prompts or code.

More about security
11Cases

Agents at work

Every case comes with the agent’s tools and its limits. For now these are demo scenarios, and we label them that way.

  1. 01Demo ScenarioAI Director: a manager’s tasks under controlThe agent takes the manager’s tasks, breaks them down, assigns owners in the tracker and sends a status with blockers every morning.Task tracker · Calendar · TelegramOpen the case
  2. 02Demo ScenarioAI customer support with handover to peopleThe agent answers from the knowledge base, checks orders in the CRM, creates tickets and hands anything beyond its authority to an operator.Knowledge base · CRM · Helpdesk · Telegram / web chatOpen the case
  3. 03Demo ScenarioAn AI analyst on top of the data warehouseThe agent answers managers’ data questions: it queries the warehouse, explains the deviation and attaches the query it used.Data Warehouse · Metric catalogue · Shared driveOpen the case
  4. 04Demo ScenarioAn AI sales assistant for inbound leadsThe agent processes an inbound lead, checks its CRM history, gathers company data and drafts an offer — the manager reviews and sends it.CRM · Email · Price list · Open company dataOpen the case
12Contact

What do you want to hand to AI?

Describe the task in your own words, answer five questions and get a preliminary AI agent schema. Or just write to us.

Message us on Telegramabdurahimmir@gmail.com

Preliminary AI agent schema

Step 1 of 7

Just message us on Telegram