AI Agent Platform

An AI agent is a program that receives a goal, decides on its own which steps to take, and calls external systems to execute them. One agent can be written in a few days and almost always works in a demo. Problems begin when there is more than one agent, they live longer than a single session, and they touch production systems.

An AI agent platform is the shared layer that handles everything around the model: starting and stopping the agent, its memory, access to data and tools, the action log, permissions, and the points where a human confirms the decision. In this setup, the model is replaceable, the platform is not.

It is important not to confuse three different things. Framework (LangGraph, CrewAI) - a library from which a developer assembles an agent in code. Visual builder (Dify, n8n, Flowise) - an environment where a workflow is assembled with a mouse and immediately gets a UI and API. Platform what remains when you have dozens of agents: a single registry, shared access to tools, observability, and governance. The first two often grow into the third, but do not become it automatically.

Gartner's forecast that more than 40% of agent projects will be shut down is not about the models. Operations break down, and they do so predictably.

Agent sprawl. Departments launch their own agents independently. Six months later, the company has two dozen of them, half have no owner, and no one can say which ones access the production database or with what permissions.

Loss of context between handoffs. An agent hands off a task to another agent, and with it the history is lost: why the decision was made, what has already been checked, what is forbidden. According to 2026 data, about 22% of production deployments already coordinate three or more agents - meaning handoff has become the norm, not the exception.

No human in the loop for high-cost actions. As long as the agent is only reading, there is no risk. Once it starts sending documents, changing prices, or writing to an accounting system, you need a point where an employee confirms the decision and an audit trail to investigate the incident later.

Nothing to show in a review. Without tracing, there is no answer to the question of why the agent did it. That stops adoption faster than any technical limitation.

The two standards address different tasks and are used together in 2026 projects.

MCP describes how an agent gets access to a tool or data source. This is the vertical agent-to-system connection. By February 2026, the protocol SDKs were being downloaded about 97 million times per month, all major model providers had adopted it, and API gateway vendors were embedding MCP support into their products.

A2A describes how agents coordinate with each other and hand off tasks - the horizontal connection. As of April 2026, more than 150 organizations were using the protocol in production. In March 2026, Huawei announced the open sourcing of A2A-T, an industry extension for telecom.

The practical conclusion is simple: tools connect through MCP, and task handoff between agents happens through A2A. A platform that supports neither will lock you in at the integration layer.

KT.Team deploys the listed open platforms within the customer's environment: we set up orchestration, connect enterprise sources through RAG and MCP, put in place a model access gateway with request logging, and configure tracing and permissions before the agent gets write access.

The working approach is the same as in our other AI projects: we take one process, bring it to a working state and acceptance, and only then expand the scope. Details on related topics are on the pages LLM gateway, MCP, n8n and an enterprise knowledge base on RAG. Federal Law GDPR requirements are covered separately: compliance with Federal Law GDPR and protection of personal data information systems.