Building AI agents from scratch using the OpenAI Agent SDK and Modal.

Building custom AI agents requires a robust "harness"—the supporting infrastructure surrounding an LLM—to manage context windows and prevent performance degradation known as "context rot." Effective agent design relies on strategies like sub-agents, which encapsulate complex tasks to keep the primary orchestrator's context window lean, and progressive disclosure, which feeds information to the model only when necessary. Coding agents, specifically, benefit from sandboxed environments and programmatic tool calling to handle unpredictable outputs without bloating memory. Developers should prioritize modularity, using tools like GPU-limited pools to maintain control over compute costs. While rigorous evaluation through software-like tests is ideal for production, building effective harnesses often involves iterative experimentation and balancing memory management with task-specific requirements to ensure agents remain performant over long-horizon workflows.