AI Agents & Multi-Agent Systems
AI agents that do real work in your systems, with a person in control.
Single agents with tools or orchestrated teams of specialist agents — tool calling, MCP, LangGraph or custom orchestration, running on cloud model APIs or on private models.
AI AGENTS, BUILT FOR OPERATIONS
From chat answers to finished tasks.
An agent reads, decides and acts through the tools you allow — and stops when a person needs to approve.
A chatbot answers questions. An agent goes one step further: it reads a ticket or a document, looks up data in your systems, decides what to do next and calls tools to do it — create an order, update a record, draft a reply, open a ticket. Done well, this removes hours of repetitive back-office work every week. Done carelessly, it produces a fast, confident system that makes mistakes in production and that nobody can audit.
We build agents the way we build any production software. Our team has been shipping and running software since 2013: clear scope, explicit permissions, tests, logs and a person in the loop wherever an action has real consequences. Agents connect to your systems through well-defined tools and MCP integrations, and they can run on cloud model APIs or on private models hosted on our own GPU cloud in Cluj-Napoca.
Single agents with tools
One agent, a small set of well-described tools and a clear goal: triage an inbox, fill a form from a document, prepare a report. Often the simplest design that works, and the one we try first.
Multi-agent teams
An orchestrator splits the work and hands it to specialist agents — researcher, planner, executor, reviewer — each with its own instructions, tools and limits. Useful when a task has distinct stages or needs an independent check.
Guardrails and operations
Human approvals for sensitive actions, permissions per agent and per tool, audit logs, evaluation suites, cost budgets and tracing, so you can see what every agent did and why.- Use-case analysis and an agent design document
- Tool and API layer for your systems, via function calling or MCP
- Single-agent or orchestrated multi-agent workflows
- Human-in-the-loop approvals in the tools your team already uses
- Least-privilege permissions per agent and per tool
- Audit log of every prompt, tool call, decision and approval
- Evaluation suite built from real examples, run on every change
- Cost control: budgets, model routing, caching and step limits
- Tracing, dashboards and alerts for agents in production
- Deployment on cloud model APIs or private models on our GPU cloud
WHERE AGENTS FIT
Repetitive work, clear rules, several systems
Typical first projects — narrow, measurable and easy to review.
Order and support triage
Read incoming emails and tickets, classify them, pull order and customer data, then propose a reply or route the case to the right team. A person approves anything that changes an order or refunds money.Back-office data entry
Extract data from invoices, forms or emails and enter it into your ERP, CRM or e-commerce admin, with validation rules and a review queue for the fields the agent is not sure about.Report generation
Collect figures from databases and internal tools, compare them with the previous period, explain the differences and draft a weekly or monthly report for a person to finalise.Procurement checks
Check purchase requests against budgets, contracts and approved supplier lists, flag mismatches and prepare the approval packet for the person who signs.Internal IT helpdesk
Answer common questions from your internal documentation, run safe routine actions such as access requests, and escalate everything else with full context.Your own process
If a process is documented, repetitive and spread across several tools, it is a candidate. We will also tell you honestly when a plain script or a fixed workflow is the better answer.Stack we work with: tool calling and MCP, LangGraph or custom orchestration in Python or TypeScript, queues for long-running jobs, PostgreSQL (with pgvector where retrieval is needed), OpenAI and Anthropic Claude APIs, and open-weight models such as Llama, Mistral or Qwen served with vLLM.
HOW WE WORK
From one process to a reliable agent, in clear steps
Start narrow, measure everything, widen the scope only when the data says so.
Pick one process
We map one concrete process with the people who do it today: inputs, systems touched, decisions, exceptions and what a mistake would cost.
Design agents and tools
We decide whether one agent is enough or a team is needed, define each tool and its permissions, and mark the steps that need human approval.
Build the evaluation set
Before tuning prompts we collect real examples with expected outcomes, so quality is measured, not guessed. More on AI testing and evaluation.
Run in shadow mode
The agent works on real inputs but only proposes actions. Your team compares its proposals with what they would have done and tells us where it is wrong.
Go live with approvals
Low-risk actions run automatically; sensitive ones wait for a click from a person, in the tools your team already uses. Every step is logged.
Monitor and improve
We watch traces, cost and error rates, add new cases to the evaluation set, and widen the scope only when the results support it.RISKS WE HANDLE
What can go wrong, and how we design for it
Agents act on real data, so failure modes are planned for from the first design.
Wrong or duplicate actions
Idempotent tools, validation before every write, approval gates for anything that cannot be undone, and a clear way to roll back.Prompt injection
Untrusted content such as emails, web pages or documents never gets to grant permissions or change instructions. We test for it explicitly, as part of AI security and red teaming.Data exposure
Agents see only the data their task needs. Sensitive workloads can run on private models, so prompts and documents stay on infrastructure we operate.Runaway cost
Step limits and token budgets per task, smaller models for simple steps, caching for repeated lookups, and alerts before a budget is reached.Silent quality drift
Regression evaluations on every prompt, model or tool change, plus regular sampling of production runs for human review.Opaque decisions
Every run is traced end to end, so for any task you can answer what the agent saw, which tools it called, who approved and why.FAQ
Questions we hear about AI agents
What is the difference between an AI agent and a chatbot?
A chatbot answers questions in a conversation. An agent works towards a goal: it can call tools, read and write data in your systems and take several steps on its own, within the limits you set. That is why agents need permissions, approvals and audit logs from day one.
Do we really need a multi-agent system?
Often not. We start with the simplest design that meets the goal, usually one agent with well-described tools. Several agents make sense when the stages of a task are clearly different, when an independent reviewer adds real value, or when parts of the work can run in parallel.
Can the agents run without sending data to external AI providers?
Yes. Agents can use open-weight models such as Llama, Mistral or Qwen, served on our GPU cloud in Cluj-Napoca or on your own infrastructure. Cloud model APIs remain an option for the steps where they fit better, and the two can be combined.
How do you stop an agent from doing something harmful?
Each agent gets only the tools and data its task needs, sensitive actions wait for a human approval, every step is logged, and before go-live we test the system against wrong inputs, edge cases and prompt injection.
How are agent projects priced?
After a short discovery we propose a fixed scope for the first process, a dedicated team, or a value-based model tied to the results. The options are described on our AI engagement models page.
Have a process an agent could take over?
Describe the task, the systems involved and who approves it today. We will tell you honestly whether an agent is the right tool and what a first version would look like.