Infrastructure for AI Agents: The End of Old DevOps

All modern cloud infrastructure is designed for humans: one long-lived session, predictable workloads, and minutes of waiting in CI or during deployment—an acceptable price.

Agent workloads are different: thousands of short, isolated runs per hour, each requiring a clean environment; a GPU is often needed for just a few seconds and then released immediately.

A conventional VM takes a minute to start, a container takes tens of seconds, and a cloud GPU queue adds just as much again. A company running an agent product on this infrastructure must choose between two losing options: keep a pool of resources warm but idle and overpay, or endure a cold start lasting several minutes for every agent call.

In the second case, TTU—the time to outcome—is dominated by infrastructure wait time: the model's seconds disappear into minutes of cold-start delay.

As models become smarter, the harness around them—call orchestration, memory, and action control—shifts the human role from line-by-line model supervision to attention management: deciding where human review is needed.

The infrastructure and orchestration beneath this harness only become more important: the more autonomous the agent, the higher the cost of every second of downtime and the more expensive a poorly designed layer between the agent and real actions in company systems becomes.

For a company evaluating an agent project, the right question is not “which model,” but what latency and cost profile will result under bursty, concurrent agent workloads—and who owns the infrastructure when it fails.

The agent's brain is almost always externalized into the Claude, GPT, Gemini, YandexGPT, or GigaChat API. Connecting that brain to real application actions—through MCP servers, an LLM & Security Gateway, model routing, and secure access to production data—is weeks of engineering work that companies consistently underestimate at the outset.

This is precisely where KT.Team delivers projects: at the intersection of agent integration with real systems, rather than model selection. AI-native integration on top of an existing 1C, Pimcore, or Bitrix system requires the same discipline as Modal's GPU snapshots—unnoticeable from the outside, demanding under the hood.