AI agents in production: six architectural mistakes that kill them before launch

A new breakdown on Habr examines six architectural mistakes that cause AI agents to break when they move into real production, even though they perform confidently in demos.

What you see in the demo and what happens in prod

In a demo, an AI agent looks reliable: it calls the right tools, assembles a final answer, and reports that the task succeeded. That picture is usually what convinces the team and the client that the architecture is ready for real-world use, and the project moves to launch without any extra resilience testing.

A real system behaves differently. The author lists several types of failures that surface quickly after a production launch and almost never show up in controlled demo scenarios:

• empty responses from the agent, with no explanation of what went wrong
• loops, where the agent repeats the same tool calls without making progress toward a result
• loss of context in long sessions and chains of sequential calls
• budget limits, where token or per-request cost limits are exceeded
• access-permission problems when the agent calls external systems and APIs

The Habr breakdown is organized around six architectural causes behind exactly these failures: the demo test scenario is deliberately narrow and predictable, while the system's behavior in real-world use is not.

Why a demo doesn't guarantee stability in prod

A demo scenario typically follows a single, known path: a limited set of inputs, a clean environment, and no concurrent requests from other users. That setup hides architectural weaknesses that only appear with varied user requests, long conversations, and real infrastructure constraints such as API quotas, external service timeouts, and access permissions for sensitive data.

That's why moving AI agents from prototype to production is harder than it is for conventional software services. The agent makes decisions dynamically at every step, and any gap in error handling or state management doesn't show up right away. It surfaces only after a certain number of steps, a certain volume of context, or a certain number of parallel sessions has accumulated. A bug that goes unnoticed in a short five-step demo becomes systemic by step fifty of a real conversation with a user.

Which parts of the architecture to check first

Judging by the failure categories listed, the Habr breakdown focuses not on the quality of the model underlying the agent but on the engineering harness around it: how the system handles tool errors, caps retries, stores and trims context, tracks spend against the budget, and checks permissions on every external API call. These are exactly the areas a demo scenario usually doesn't stress, because a demo lasts a few minutes and reuses the same pre-prepared path.

For teams preparing an agent for launch, this means a dedicated stage of load and scenario testing, separate from standard functional acceptance. Teams need to deliberately trigger empty responses from external services, long conversations that pile up context, and missing-permission situations to see how the agent behaves outside the happy path.

What it means

The piece is a reminder that an AI agent working in a demo says nothing about how resilient it will be in prod. Before rolling an agent out into a real system, it's worth separately testing empty-response handling, loop protection, context management in long sessions, budget limits, and access permissions — the exact parts of the architecture where, according to the author's observations, failures happen most often. Teams that skip this stage risk ending up with an agent that sails through the client presentation but stops working within its first week of real-world use.