AI-Native
AI-native integration: standard agent access to enterprise systems
For the client's existing services, KT.Team builds MCP servers and agent connectors: LLM agents and tools access enterprise systems through one open standard instead of a set of point integrations for each model.
AI-native integration: MCP servers and agent connectors on top of existing systems
Our clients
Clients and partners
Standardone interface for agents to access systems instead of N×M point integrations for each model
~40%enterprise applications will have task-specific AI agents by the end of 2026, according to Gartner's forecast (up from less than 5% in 2025)
Unitscompanies have taken agents to production - the gap from experiments is closed by the engineering layer (Gartner)
Freedomswitching models (Claude, ChatGPT, Gemini, Copilot) and vendors without rewriting integrations
Industry solutions
What can be done with AI-native integration
Capabilities
AI-native integration capabilities
Enterprise services: ERP, CRM, PIM, warehouse, billing, DBESB/bus (DATAREON): guaranteed delivery and loose coupling between systemsMCP servers: each source is wrapped in resources and tools to the standardMCP gateway: OAuth authorization, scopes, PII obfuscation, call auditingLLM gateway: model routing, budgets, observabilityAgents and LLMs: Claude, ChatGPT, Gemini, and Copilot call toolsBusiness scenarios: data answers, actions in systems, automation
Agent access to up-to-date data
Agents access ERP/CRM/PIM/warehouse through one interface and see live data, not exports
End of integration spaghetti
Instead of N×M custom connectors for every system-model pair, one MCP server per source, reusable by all agents
Portability and no vendor lock-in
Open standard: hand off the integration to another team, switch models (Claude, ChatGPT, Gemini, Copilot) without rewriting it
Faster scenario delivery
A new agent scenario is assembled from ready-made MCP tools with prompts, not as a from-scratch integration project
Controlled access
Separate what the agent can do from what it can access through a gateway: authorization, scopes, and audit of every tool call
Data protection
Personal data obfuscation and policies at the gateway level before data goes to an external model
Corporate Memory
System knowledge and context are reused across agents instead of being embedded in one prompt for one model
Evolution without rework
Loose coupling: the source system can be replaced without changing the agents on top of it
Approach
How we implement AI-native integrations
Without modifying the core
We do not fork or patch the core of the AI-native integration. The AI-native integration stays on the standard updatable version - we move business logic into separate adjacent microservices, so platform updates do not break your custom work.
International Standards, Not Homegrown Hacks
Where a mature international solution exists, we use it instead of inventing our own protocol or platform. Before writing code, we study how the problem is already solved in the industry.
Transferability
The solution is loosely coupled and documented: it can be handed over between teams and contractors without rewriting. You are not tied to us.
AI compatibility
AI-native integrations in the AI stack
MCP servers
Each enterprise service is wrapped in an MCP server with defined resources and tools according to the 2025-11-25 specification
Tools
Actions and system queries as typed tools available to any MCP-compatible agent
Agents and models
Claude Opus 4.8 / Sonnet 4.6, ChatGPT, Gemini, Copilot - one interface; the agent connects to the server without knowing its internals
Security and gateway
MCP gateway as a control point: OAuth authorization, schema validation, PII obfuscation, auditing, and tool call limits
Context 2026
What changed in the market
MCP is the access standard
In the first year, SDK downloads and the number of public servers grew several times over; OpenAI, Google, Microsoft, and Salesforce announced support for the standard
Neutral foundation
MCP is governed by the Agentic AI Foundation under Linux Foundation (co-founders Anthropic, Block, OpenAI), which reduces the risk of vendor lock-in
Production readiness
The 2025-11-25 release added async Tasks, modern OAuth authentication, and extensions, moving from synchronous calls to managed long-running processes
Security focus
Gartner predicts that more than 40% of agentic AI projects will be canceled by the end of 2027 due to cost, unclear value, and governance gaps; the control point is the MCP gateway
Gap from production
Most companies are experimenting with agents, but only a minority have taken them to production (Gartner); the gap is closed by the engineering layer, not a demo
Honestly
Pros and cons
Pros
- One standard interface instead of N×M custom connectors: integrations are reused and do not need to be rewritten when the model or vendor changes
- Portability and no vendor lock-in: the open standard is backed by OpenAI, Google, Microsoft, Salesforce, and governed by a foundation under Linux Foundation
- Loose coupling: the source system can be replaced without changing the agents on top of it
- Controlled access through a gateway: authorization, scopes, PII obfuscation, and audit of every tool call
- Speed: new agent scenarios are assembled from ready-made MCP tools, not built as an integration project from scratch
Cons
- MCP does not define security on its own - without a gateway with OAuth, scopes, and auditing, "open doors" appear: in early 2026, several vulnerabilities were found in MCP, including RCE
- Gartner’s main risk is confusing what an agent can do with what it can access; Gartner predicts over 40% of agentic AI projects will be canceled by the end of 2027
- The standard is changing quickly (current specification: 2025-11-25): server compatibility must be maintained
- Result quality is limited by the quality of data and contracts in the source systems - MCP does not fix dirty data, it makes it available to the agent
Blog
Articles about AI-native integrations
Projects
Cases
AI ingredient recognition by barcode
- Processing sped up from 30 minutes to 2 per batch of 10 images
- Recognition accuracy for composition is 80-95%
OSNO-VA: AI accountant
- Built an AI platform
OSNO-VA: Financial Agent on MCP
Learn moreDiscuss AI-native integration: standard agent access...
Related solutions
Articles on the topic
- ESB Systems for Integration Comparison: 2026 Overview
- How Much an IT System Really Costs and Why Development Is Only the Tip of the Iceberg
- Do You Always Need a Message Broker?
- Point-to-Point, Broker, ESB: Which Integrations Help Build a Loosely Coupled IT Architecture
- How to Choose the Right Type of Integration for Your IT Architecture
