AI
When a company runs dozens of assistants, the main risk is not the quality of a single agent but loss of control: who owns it, what data it sees, what it is allowed to do and who is accountable for errors. KT.Team builds a unified agent control layer as part of the corporate architecture.
AI layer
Core thesis of the AI block: a pilot with measurable impact, private data under control, agent actions logged, quality passes evals before scaling.
A chatbot answers; an assistant checks the regulations, queries systems, records the deviation and proposes the next step.
Agent registry, owner, permissions, memory, evals, trace logs, kill-switch and budget at the enterprise-layer level.
RAG returns an answer with a source citation; LLM Gateway obfuscates personal data before the model and restores it after the response.
Five assistants can still be kept as a set of prompts and instructions. Dozens of assistants in a large company require a management system: who owns each one, what data is available, which actions are allowed, how results are verified, where memory is stored and how errors are investigated. KT.Team builds this control layer as part of the corporate architecture, not as scattered experiments.
The assistant registry records the owner, role, permissions, integrations and area of responsibility.
Corporate memory stores documents, regulations, decisions, project history and work instructions in a portable format. AgentOps provides logs, quality control, evals and action tracing.
Security defines access rights, data perimeters, approvals and audit.
Integrations connect Jira, Git, 1C, DWH, Slack, email, EDI and internal APIs.
Results are measured not by the number of agents but by how the company's work changes: fewer manual approvals, faster document preparation, fewer data errors, shorter team onboarding and clearer accountability for decisions. For each assistant we define an owner, metrics, allowed actions and quality criteria.
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