AI agents - Articles

Incorrect model assumptions may remain invisible for a long time. The result? Technical debt that is difficult to trace back to a single design decision. As agent frameworks gain traction, IT teams need to answer one practical question: how can they use nondeterministic AI models without losing control over code quality, costs, and system stability?

Simple code suggestions in the IDE are no longer enough. As autonomous agent systems move into mainstream engineering workflows, teams are shifting their focus from generating code to designing scalable, predictable architectures. Agent frameworks can deliver a real competitive advantage — but only when they give you control over data flow, cloud costs, and model behavior.