Scenario and limits of authority
We define what the agent does on its own, what it proposes to a human and what it never does. The limits are set in the architecture, not by a request in the prompt.
AI agents · Assistants · Task automation
AI agent development: autonomy you can trust with the work
An agent differs from a chatbot in that it acts: it chooses its own steps, calls your systems and carries the task through to a result. So the main question is not “which model” but what authority it has, how its work is checked and what happens when it makes a mistake.
For teams that need to take repetitive work that still requires thought off people: parsing requests and documents, first-line support, preparing reports, routine inside CRM and internal services.
What is included
Tools and access to systems
We connect the agent to your databases, APIs and services — with handling for failures and timeouts. A tool will return an error one day, and that is exactly the moment the agent must behave predictably.
Company knowledge
Documents, internal procedures and data become the agent’s memory through semantic search and knowledge graphs — so that it relies on your rules, not on the model’s general notions.
Quality evaluation
Sets of test tasks and metrics before rollout to production. Without them, “it got better” is a feeling, not a fact, and every prompt change turns into a lottery.
Audit and observability
Every agent action is logged: what it did, on what grounds and what it cost. This is needed both for investigating incidents and for controlling spend on model calls.
Interfaces
The agent lives where people work: a messenger, a web interface, an embedded widget, an internal system. A separate service that people have to make a point of visiting loses its users.
How we work
Choosing the task
We pick a scenario that repeats often and has a verifiable result. Rare and one-off tasks are not handed to an agent.
Prototype with evaluation
We build the agent together with a set of test tasks: quality is measured from day one, not after users complain.
Pilot with people
We launch on a real flow of work with human confirmation. We look at where the agent makes mistakes and on what exactly.
Production
We extend autonomy where quality is confirmed and keep control where the cost of an error is high.
Operation
Monitoring quality and cost, further training on new cases, regression checks when the model changes.
Why us
We run agents in production
We develop and run our own agent orchestration system and use it in our daily work. We are both its authors and its users — and we know where it breaks.
Engineering instead of prompt magic
Versioning, tests, metrics, regressions. We treat an agent as software that will live in production for years, not as a lucky conversation.
Working inside a closed perimeter
If data cannot leave the perimeter, the agent is deployed on local models inside your infrastructure.
Cases
Aviation — aircraft components trading and MRO
PilotAircraft parts procurement: from inbox chaos to a managed pipeline
An AI agent reads incoming RFQs, compares supplier quotes and checks trace documents; an operations system moves each deal through mandatory stages.
- Full loop
- full AI loop on a live procurement process — from incoming RFQ to team notification; confirmed by the owner
- 3
- types of trace documents checked by the agent: FAA 8130-3, EASA Form 1, COC; counted from the owner's brief
Distribution · Partner networks
In productionA sales and support platform for a distributed partner network
Partners ask who on their team is inactive and what blocks the next level — and get numbers for their own part of the network only, enforced at the SQL level.
- 20 of 21
- UAT checks passed on the client's live back-office database (reconciliation protocol); one partial — no last-activity date in the data
- 10 of 10
- question categories from the assistant behaviour spec (~25 scenarios) implemented; one sub-item outstanding (checklist count)
Music tech · SaaS
MVP in operation (closed launch)Music releases without missed deadlines
Release ops for independent labels: 9-stage lifecycle, pitching deadlines for 12 stores, AI drafts with human approval. Zero to production-grade in 7 weeks.
- 7 weeks
- from first commit to a production-grade stack with Vault, encrypted backups, SLOs and a move to a Russian data centre (265 commits)
- 16 days
- from project start to an investor presentation with a live product demo
AI infrastructure / multi-agent systems
In-house productAgent teams assembled by configuration that review their own work
Core of a multi-agent platform built in 2.5 weeks: 29 declarative agents in three teams, agent-critic pairs, shared memory on four databases, five autonomy levels.
- 25,000+
- lines of Python in the platform core across 134 modules, counted from the repository
- 29
- declarative agents in three ready-made teams — engineering, cognitive, research; counted from YAML specs
IT consulting · Lead qualification
In-house productA pilot plan for the visitor, a qualified lead for sales
We replaced the contact form with an AI consultant: it interviews the visitor, returns a pilot plan with KPIs and risks, and sales gets a lead with budget and timeline.
- 1 day
- Working MVP — frontend, backend and deploy configs — built in a single day
- 16 days
- From first commit to production with HTTPS and an issued certificate on its own domain
Frequently asked questions
How is an AI agent different from a chatbot?
A chatbot answers with text, following a preset script or a model. An agent performs a task: it plans the steps, calls external systems, checks the result and sees the job through. Conversation is the agent’s interface, not its work.
How much does it cost to develop an AI agent?
Most of the cost is not in the agent itself but in the integrations, preparing the knowledge and the quality-evaluation loop. A simple agent with one data source and one integration point costs noticeably less than an agent with access to several systems and a requirement to run inside a closed perimeter. We name a range after reviewing the scenario.
What happens if the agent makes a mistake?
Exactly what the architecture provides for: actions with a high cost of error require human confirmation; the rest are logged and reversible. We design the system on the assumption that an error will happen — because it will.
Can the agent run on a local model?
Yes, if the perimeter requires it. Local models give lower quality on complex reasoning, so the scenario is matched to what the model can do — and this is discussed up front, not discovered at the end.
How do you measure the quality of an agent’s work?
With a set of test tasks whose correct result is known, plus metrics on the real flow: the share of tasks completed without intervention, the share corrected by a human, the cost of a single task. The same metrics show when autonomy can be extended.
Do we need a multi-agent setup straight away?
Usually not. One agent with good tools is cheaper and more predictable. Several agents are justified when you need different roles with different permissions, or when one participant checks another’s work — there is a separate page on multi-agent systems for that.
Related services
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