Kytrai Agent OS
A command centre for multi-agent AI work: permissions, budgets, approvals and logs.
AI agents that do well-defined work under control.
We build AI agents and agent systems for businesses. An agent carries out multi-step tasks with your own tools and data — with scoped permissions, full logging and human approval where it matters.
01 Definition
What is an AI agent?
An AI agent is software in which a language model does more than answer questions: it plans and carries out a task step by step. It can look up information, call APIs, work with files and check intermediate results before moving on.
An agent only has the tools and permissions it is given. It is not an independent employee but part of a system whose limits and oversight are designed in advance.
02 Use cases
What an AI agent can do
Information retrieval
Finds and combines information from company documents, databases and other sources.
Tool use
Works with tools such as a CRM, ticketing or a calendar within the permissions it has.
API integrations
Reads and updates data in company systems through their APIs.
File processing
Reads, summarises and extracts information, and drafts reports.
Multi-step processes
Works through a task one step at a time and checks intermediate results.
Agent teams
Several specialised agents split the work; a coordinating agent assembles the result.
03 Comparison
AI agent vs. chatbot
| Chatbot | AI agent | |
|---|---|---|
| Task | Answers a question in a conversation | Carries out a task step by step |
| Tools | Usually does not use other systems | Uses APIs and tools within given permissions |
| Knowledge | General model knowledge, possibly a knowledge base | Company knowledge base and live system data |
| Output | A text reply | Work done: updated data, a draft or a report |
| Oversight | Conversation monitoring | Permissions, approval gates and logs |
04 Safety
Oversight is designed before the agent does anything
Scoped permissions
The agent can only reach the systems and actions its task requires.
Per-tool permissions
Read and write access are separated, and every tool has its own limits.
Approval gates
A person approves risky actions such as sending messages, payments or deleting data.
Logging and audit trail
Every tool call and decision is recorded for later review.
Budgets and limits
Cost and iteration limits prevent runaway execution.
Human control
The agent can be stopped, and uncertain cases are routed to a person.
05 Limitations
What not to expect from an agent
Language models can make mistakes and misread instructions. That is why agents get tasks whose results can be checked, and critical steps are left for human approval. We do not build agents that act without oversight in everything.
When the need is to answer questions or summarise documents, a lighter AI solution is often enough.
AI solutionsRelated work
How a project runs
Six phases, the same people.
- 01
Definition
Goal, users, constraints and the smallest useful first release.
- 02
Design
User flows, data model, architecture and interface are designed before the build.
- 03
Build
Built in short iterations, with a working version visible throughout.
- 04
Testing
Automated tests, accessibility and security are checked before release.
- 05
Release
Deployment, monitoring and, where needed, migrating data from the old system.
- 06
Ongoing development
Fixes, improvements and new features based on real use.
AI agent FAQ
- What is the difference between an AI agent and a chatbot?
- A chatbot answers questions; an AI agent carries out tasks. An agent can use company systems, look up information and work through a multi-step process. That is why its permissions, approvals and logging are designed more carefully than a chatbot's.
- Can an AI agent use our own systems?
- Yes, if the system has an API or another controlled way to integrate. The agent only gets the permissions its task needs, and write actions can require human approval.
- How is company data protected?
- We first define what data the agent may process. Permissions are scoped, credentials stay on the server side and processing is logged. The language model provider and its data processing terms are chosen to meet your requirements.
- Does an AI agent work completely on its own?
- No, and it usually should not. The agent handles well-defined steps itself, but risky actions go through human approval. It can also be stopped at any time.
- Where should an AI agent project start?
- With one recurring, well-defined task whose success can be measured. Once the first agent works reliably, it is easier to extend it to new tasks or an agent team.
- How much does an AI agent cost?
- It depends on the number of tasks, the integrations and how much oversight is needed. Language model usage also has running costs, which are estimated in advance. You get an estimate before development starts.
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Is there a task an agent could handle?
Tell us which recurring work takes time and which systems it touches.
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