AI Agents in Production: Six Architectural Errors That Prevent Them From Reaching Launch

A Habr article breaks down six architectural errors that cause AI agents, which appear reliable in demonstrations, to fail when deployed to real production.

What is visible in a demo and what happens in production

In a demonstration, an AI agent looks reliable: it calls the necessary tools, collects the final answer, and reports successful task completion. This picture is what typically convinces both the team and the client that the architecture is ready for operational deployment, and the project moves to launch without additional resilience checks.

In a real system, things are different. The author lists several types of failures that quickly surface after production launch and almost never appear in controlled demo scenarios:

• empty agent responses with no explanation of failure cause
• loops — the agent repeats the same tool calls without making progress toward a result
• context loss in long sessions and chains of sequential calls
• budget constraints — exceeding token limits or the cost of a single request
• access permission issues when the agent accesses external systems and APIs

The Habr analysis revolves around six architectural reasons that lead to exactly these failures: the test scenario in a demo is intentionally narrow and predictable, while real system behavior is not.

Why a demo does not guarantee production stability

A demonstration scenario typically follows a single known path: a limited set of input data, a clean environment, and no competing requests from other users. This environment hides architectural weaknesses that appear only with diverse user requests, long dialogues, and real infrastructure limitations — API quotas, external service timeouts, access rights to sensitive data.

This is why the transition from prototype to production for AI agents is more difficult than for ordinary software services: the agent makes decisions dynamically at each step, and any shortfall in error handling or state management does not appear immediately, but only after accumulating a certain number of steps, volume of context, or number of parallel sessions. An error invisible in a short five-step demonstration becomes a systemic failure by the fiftieth step of a real user conversation.

What architecture nodes are checked first

Based on the categories of failures listed, the Habr analysis focuses not on the quality of the model underlying the agent, but on the engineering infrastructure around it: how the system handles tool errors, limits retry attempts, preserves and trims context, counts spent budget, and checks permissions for each external API call. These are precisely the places that a demo scenario typically does not stress — because the demonstration lasts only a few minutes and uses a single pre-prepared path.

For teams preparing an agent for launch, this means the need for a separate stage of load testing and scenario testing, different from ordinary functionality acceptance: you need to intentionally provoke empty responses from external services, long dialogues with accumulated context, and situations of insufficient permissions, to see how the agent behaves beyond the happy path.

What this means

The material reminds: an AI agent's performance in a demo says nothing about its resilience in production. Before rolling out an agent to a real system, it is worth separately checking empty response handling, protection against looping, context management in long sessions, budget limits, and access rights — that is, exactly those architecture nodes where, in the author's observations, failures most often occur. Teams that skip this stage risk getting an agent that passed a beautiful presentation to a client but is non-functional in the very first week of real operation.