Registry and ownership
Agent card: owner, role, permissions, integrations, kill-switch, and budget.
AI
Five assistants are just a set of prompts. Dozens of agents in a large enterprise require a governance framework: who owns what, which data is accessible, what actions are permitted, and how incidents are investigated. KT.Team builds this framework as part of the corporate architecture, not as scattered experiments.
Our clients
| System / layer | Scope of responsibility |
|---|---|
| Owner | agent registry: owner, role, permissions, integrations, scope of responsibility; each agent has a kill switch and budget |
| Data Access | data scopes, access controls, and approvals instead of shadow agents running on production data |
| Permitted actions | explicit list of permitted agent actions and emergency stop |
| Audit & Tracing | every action is logged with its owner and scope; errors can be investigated via the trace |
| Memory | a single portable memory layer (Sloy) instead of duplicates and inconsistencies across agents |
| Quality (AgentOps) | logs, evals, and quality control for every agent |
Owner, role, permissions, and integrations are recorded in a single agent registry.
Portable memory layer (Sloy) with access to Jira, Git, 1C, DWH, Slack, email, EDI, and internal APIs.
Allowed actions, data boundary, and quality criteria - what the agent may do and how we verify the result.
Logs, evals, and action tracing in production.
Traces show who the owner is, which data perimeter the agent operated in, and what went wrong.
We measure how the company's operations change, not how many agents are running, and adjust the scope accordingly.
Agent card: owner, role, permissions, integrations, kill-switch, and budget.
Documents, policies, decisions, and project history in a single portable layer.
Data boundaries, access, and audit; sensitive requests go through the LLM gateway for Federal Law 152.
Logs, evals, quality control, and action tracing - a mechanism, not a slogan.
Jira, Git, 1C, DWH, Slack, email, EDI, and internal APIs through a managed integration layer.
The OSNO-VA AI accountant is a separate agent with an owner, permissions, and audit.
Success is measured not by the number of agents, but by how operations improve: fewer manual approvals, faster document preparation, fewer data errors, shorter team onboarding, and clearer accountability for decisions.
For each agent, we define an owner, metrics, allowed actions, and quality criteria. Sloy is the memory layer (data).
This page is the control plane for many agents: who owns them, what is allowed, and how audit works.
The data boundary is the operational expression of our AI principles.
FAQ
In the registry, each agent has an owner, role, and permissions - they are responsible for allowed actions and incident review.
Agent operations as an engineering discipline: logs, evals, quality control, and action tracing to keep behavior auditable.
Full tracing: every action is recorded with its owner and data perimeter, so you can see what happened, why, and which next step reduces the risk of recurrence.
Cases