AI agents
Choosing where agents help, what they need to know, and when a human should step in.
atsco’s lab / Independent engineering
Building useful systems with AI.
I’m Artem. I build AI agents for business workflows and share the engineering behind them.
02 / The ambition
Ambition needs engineering.
Start with the outcome. Define what the system must deliver before choosing the technology.
03 / The system
Give every part a purpose.
Models reason. Tools act. Code checks. People own the decisions that need judgment.
04 / The agents
From a task to a verified outcome.
A simple example: research a brief, draft the work, then check the result before delivery.
Inside the lab ↓Task + contextGoal · constraints · success criteria
Agent 01ResearchSearch + sources
Agent 02CreateDraft + tools
Deterministic checksFormat · evidence · task requirements
Passed ↓Uncertain ↓
Deliver resultSave evidence
Human reviewResolve or revise
Illustrative workflow · add agents only when useful
Inside the lab
From an idea
to a useful system.
Engineering systems
Connecting models, code and tools. Testing what happens when the output meets the real world.
Building products
Turning business workflows into useful products—and understanding what makes them worth building.
From the lab
Writing / 001Engineering notes.
My AI Agents Passed Every Internal Check. Avito Rejected the File.
What a rejected file taught me about output contracts, agent responsibilities, and what “done” actually means.
Read Agent FactoryField tools / Agent Factory
Before you build
an AI agent.
Turn one business task into a clear specification before choosing the architecture.
Get the worksheet on TelegramFree. In English. PDF / DOCX / TXT.
Inside the worksheet
01
Task Contract
Inputs, outputs, constraints and acceptance.
02
Responsibility Map
Code, model, agent, workflow or human.
03
Acceptance checks
A usable result, with evidence to match.
04
Worked example & prompt
Apply the method to your own task.