The Real Engineering Challenges Teams Face When Deploying LLMs in Production

Engineers working on production AI systems warn that the term 'agent' is being applied so loosely — to simple scripts, chatbots, and tool-calling functions — that it is causing genuine engineering missteps. A more precise definition holds that a true agent must set its own next steps, recover from failures, and know when a goal is complete, rather than waiting for human instruction at each stage. In practice, most successful deployments are narrow and purpose-built, handling tasks like document extraction or customer support triage rather than general reasoning. Teams achieving strong results focus on clean tool design, robust failure handling, and full observability into decision-making, rather than simply swapping in the latest frontier model. The fragmented landscape of orchestration frameworks — LangChain, CrewAI, AutoGen, and others — adds further complexity, with new options emerging monthly before older ones are fully evaluated.