Letting an agent handle planning end-to-end leads to production incidents where it loops and stalls. The guide published by OpenAI addresses this head-on. It starts from the premise that an agent is not an intelligent being but an automation program that uses tools. Predictability Before Autonomy Do not hand over the entire flow to the model. Structure it like a state machine and explicitly guide the system to make decisions at each step. A system without control causes incidents in production. Tool calls follow the same principle. Just passing an API spec is not enough. Input data formats must be strictly constrained, and when errors occur, the cause is fed back to the model. Connecting errors to a retry loop instead of just surfacing them increases the success rate. Hallucination issues can be significantly reduced just by adding a few examples to the instructions. Design the Memory Structure Before Plugging In RAG Plugging in RAG does not immediately improve performance. As conversations grow, resource consumption rises, and without filtering for only relevant data, response speed just gets slower. Separating fixed information (user profiles, etc.) from variable information (current conversation) and managing them independently reduces token costs. Do not defer the evaluation framework. Before modifying prompts, establish quantitative metrics that distinguish success from failure. Running hundreds of test cases repeatedly and accumulating scores is what reveals bottlenecks. The Model Is a Component Tasks requiring precise calculation or strict specifications belong to external code or specialized libraries. Cramming every rule into a single long instruction makes maintenance impossible. Splitting functions and ensuring each module performs a clear, single role is the entire point. Key Takeaways Developer-defined explicit workflow control is more stable in production than autonomous planning A loop structure that feeds tool call errors back to the model raises the agent’s success rate Building quantitative evaluation metrics is a prerequisite that should precede prompt engineering Related Posts Why DataNexus — The semantic gap problem and an ontology-based approach How We Chose These Four Open-Source Tools — Tech stack selection for agent systems Source https://share.google/OobJU2T2JLz7gxlim