AI agents in production: What they cost, when they fail, and when not to use them

An AI agent is a system where a large language model autonomously manages its processes, distinguishing it from traditional workflows by allowing the model to plan, call tools, and utilize memory without following a pre-written sequence by a developer. This agentic architecture is useful when the path to a goal is uncertain, as it provides flexibility and adaptability, but it comes with increased costs, complexity, and potential for unpredictable failures compared to deterministic workflows. The effectiveness of AI agents is determined by their operational reliability, which requires evaluating properties like consistency, robustness, predictability, and failure severity, rather than just success rates. In production, the non-deterministic nature of AI agents can lead to challenges like increased token consumption and security vulnerabilities like prompt injection, which necessitate architectural solutions to limit damage. Multi-agent systems can be effective for parallel tasks but often require careful coordination and context-sharing, which can be expensive and inefficient if not justified by the task's needs. Ultimately, successful AI deployments depend on a solid infrastructure, including a reliable data layer, and a clear understanding of when the complexity and cost of an AI agent are warranted.