Case study · AI & automation
A helpdesk that works like a well-coordinated team
The multi-agent AI system analyzes notifications, cooperates with company systems and automates service. People get involved where their decision is really needed.
Graphically
It's not a chatbot. This is a digital office of specialists.
In a regular office, the reception desk recognizes the topic, the analyst collects information, the specialist solves the case, and difficult decisions go to the leader. The multi-agent system reproduces the same cooperation model.
Classification agent
He reads the report, recognizes the intention and refers the case to the appropriate specialist.
Data agent
Downloads the necessary context from company systems and checks its validity.
Solving agent
Selects next steps, uses tools and prepares a solution.
Escalation agent
Recognizes uncertainty and transfers unusual matters to a human.
Service flow
From message to action in a few controlled steps
- 01
A report is received
The system receives a message from an already used service channel.
- 02
AI understands the matter
Recognizes intent, important data, and level of confidence.
- 03
Agents cooperate
The case is passed between specialists who use the right tools.
- 04
Response or escalation
The ready answer goes to the helpdesk, and exceptions go to the human.
Agents and tools
Each agent has their own set of tools
It is not one model that knows everything. A case is handed to a specialized agent, which uses specific tools wired into the client’s systems — from reading data to real actions, such as issuing a refund or retrying fulfillment.
Recognizes the ticket’s intent and routes it to the right specialist.
Status agent
- Read case
- Conversation history
- Draft reply
Fulfillment agent
- Read case
- Retry fulfillment
- Confirmation
Returns agent
- Verification of conditions
- Refund
- Confirmation
Escalation agent
- Confidence check
- Hand off to a human
Tools connect via adapters - replacing the system does not change the agent process.
marks a tool that performs a real action in the client’s systems. Every such action passes a confidence threshold, and when in doubt it goes to a human.
What makes the solution stand out?
Automation you can trust
The system was designed to operate in a real operating environment: with current data, exceptions, failures and responsibility for decisions.
AI takes over repetition. The team maintains control over unusual and sensitive matters.
A process instead of a single prompt
Each request goes through a predictable graph of decisions, roles and security.
Data-driven answers
Agents can safely use up-to-date information in the organization's systems.
Human-in-the-loop
Confidence levels, test mode and escalations keep the human right where he is needed.
Agnostic integrations
Adapters allow you to change helpdesk, data sources and models without rebuilding the process.
Automatic 24/7 service
Repetitive issues can be resolved from start to finish without waiting in line.
Full decision trace
The dashboard shows agents' next steps, tools used, time and reason for escalation.
Technology stack
The best model for a specific role
Architecture does not tie a process to a single model. Individual roles can use families OpenAI GPT and Anthropic Claude, selected for quality, speed and cost.
- LangGraph coordinates the cooperation of agents
- Python implements the AI and tools layer
- .NET integrates business systems and processes
- Event queues ensure reliability
Your process, your digital team
Do you have a queue of cases that is growing faster than your team?
We will design agents, integrations and control principles tailored to the way your organization operates.
Let's talk about the project