AI

Managing a company's AI agent fleet (AgentOps)

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

Clients and partners

Capital Group
FSK Group
SMLT
Tochno
Dogma
Sber City
FM Logistic
Danone
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Six questions that go unanswered without a governance framework

Governance Scope Responsibility Areas

System / layerScope of responsibility
Owneragent registry: owner, role, permissions, integrations, scope of responsibility; each agent has a kill switch and budget
Data Accessdata scopes, access controls, and approvals instead of shadow agents running on production data
Permitted actionsexplicit list of permitted agent actions and emergency stop
Audit & Tracingevery action is logged with its owner and scope; errors can be investigated via the trace
Memorya single portable memory layer (Sloy) instead of duplicates and inconsistencies across agents
Quality (AgentOps)logs, evals, and quality control for every agent

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Agent Lifecycle in the Governance Scope

  1. 01

    Register in Registry

    Owner, role, permissions, and integrations are recorded in a single agent registry.

  2. 02

    Connect memory and integrations

    Portable memory layer (Sloy) with access to Jira, Git, 1C, DWH, Slack, email, EDI, and internal APIs.

  3. 03

    Define Boundaries

    Allowed actions, data boundary, and quality criteria - what the agent may do and how we verify the result.

  4. 04

    AgentOps

    Logs, evals, and action tracing in production.

  5. 05

    Audit & Investigation

    Traces show who the owner is, which data perimeter the agent operated in, and what went wrong.

  6. 06

    Business Metrics Measurement

    We measure how the company's operations change, not how many agents are running, and adjust the scope accordingly.

What the framework includes

Registry and ownership

Agent card: owner, role, permissions, integrations, kill-switch, and budget.

Enterprise memory - Sloy

Documents, policies, decisions, and project history in a single portable layer.

Security & GDPR Compliance

Data boundaries, access, and audit; sensitive requests go through the LLM gateway for Federal Law 152.

AgentOps

Logs, evals, quality control, and action tracing - a mechanism, not a slogan.

Integrations

Jira, Git, 1C, DWH, Slack, email, EDI, and internal APIs through a managed integration layer.

Example of an agent under governance

The OSNO-VA AI accountant is a separate agent with an owner, permissions, and audit.

How we measure results

  1. 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.

  2. For each agent, we define an owner, metrics, allowed actions, and quality criteria. Sloy is the memory layer (data).

  3. This page is the control plane for many agents: who owns them, what is allowed, and how audit works.

  4. The data boundary is the operational expression of our AI principles.

FAQ

Frequently asked questions

Who owns the agent?

In the registry, each agent has an owner, role, and permissions - they are responsible for allowed actions and incident review.

What is AgentOps?

Agent operations as an engineering discipline: logs, evals, quality control, and action tracing to keep behavior auditable.

How to investigate an agent error?

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

AI implementation cases

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