What is an AI agent?
An AI agent is a controlled workflow that uses a model plus business rules to complete a specific task, such as triage, drafting or qualification.
AI agents
Build AI agents that do useful work, not demos.
We design AI agents around a clear job: read, classify, draft, route, enrich or prepare a decision. Every agent has inputs, limits, approval rules and monitoring.
1 job
per agent before scaling
Logged
inputs, outputs and exceptions
Human approval
for risky actions
Quick answers
What to know before you start
What should an agent not do?
An agent should not own sensitive decisions before its output quality is measured and approval rules are clear.
What is delivered?
You receive the workflow, prompts, rules, error handling, logs, handover notes and training for the people who supervise it.
Practical scope
How AI agents for business becomes a working system
We start with one workflow, not a platform decision. The first candidate is usually lead qualification, inbox triage or document review. Before any tool is connected, we define the input, owner, approval point and measurable business result.
AI agents for business can involve CRM data, inboxes, documents, spreadsheets, n8n or Make automations, ChatGPT workflows and internal AI agents. For each implementation, we document what AI may decide, what it may only draft and where a person must approve the action before it affects a client or system.
The proof base comes from Artelity's AI adoption work: 1000+ trained specialists, 50+ workshops and projects with Eesti Koolituskeskus, VOCO, Töötukassa and the Estonian Ministry of Education. The first result should be operational: task definition, workflow wrapper and a clear decision on what to scale next.
Integrations
We map data sources, CRM fields, document types, owner roles and the places where information currently moves by hand.
Measurement
We define the baseline: time spent, error rate, response speed, pipeline leakage or training adoption signal.
Safeguards
Sensitive outputs start with human approval, logs and a clear rule for when AI should escalate instead of answering.
Focus
Useful agent jobs
Lead qualification
Score and route incoming leads before a salesperson opens the CRM.
Inbox triage
Classify emails, extract the key point and prepare a response draft.
Document review
Read structured or semi-structured files and prepare the next action.
Result
Agent components
Task definition
A narrow job, accepted inputs, banned actions and quality criteria.
Workflow wrapper
n8n, Make or custom logic around the model so work is repeatable.
Supervision rules
Logs, owner, exception alerts and approval steps.
Agent build process
01
Define the job
We choose one business task and write the acceptance criteria.
02
Test with real cases
The agent runs against examples before it touches live work.
03
Launch with controls
We add logs, fallbacks and handover so the team can supervise it.
FAQ
Common questions
Is this just a chatbot?
No. A useful agent is connected to a workflow and has a specific operational job.
Can it connect to CRM or email?
Yes. Typical agents connect to CRM, Gmail, Sheets, forms, Telegram or document folders.
The next step is a short working call.
We review your current situation and decide whether the first move should be an audit, CRM setup, dashboard, automation or training.