Many AI products stop at integrating a model: user inputs a prompt, the system returns a response. But for agents to truly accomplish tasks, developers need to think beyond the model. This article breaks down the essential components of a successful agent system: a clear strategy for task decomposition, well-designed interaction patterns with users and tools, rigorous evaluation methods to measure performance, and a harness that orchestrates the entire loop. The author emphasizes that a demo can look impressive because the model is smart, but production reliability comes from the surrounding infrastructure. By treating the agent as a system rather than a single model call, teams can build solutions that handle edge cases, recover from failures, and deliver consistent results. This framework is especially relevant for technical leads and product engineers who are moving from prototypes to scalable deployments.
A practical framework for designing AI agents that work in production, focusing on strategy, interaction, evaluation, and harness.