From human-operated agent development to systematic agent improvement

Over the past year, agents have transitioned from occasional demos to integral parts of daily development workflows, with tools like Claude Code, OpenClaw, and open-source agent harnesses becoming essential. Despite their growing utility, the current human-operated development model, which involves manually identifying and fixing agent failures, is unsustainable as agents scale up. This shift was highlighted at Arize Observe 2026, where cofounders Jason Lopatecki and Aparna Dhinakaran discussed the transition from manual to systematic, automated agent improvement. The proposed architecture involves an improvement loop that includes trace collection, failure discovery, managed workers, evaluations, and fleet controls. As agents become long-running processes, they require a new approach to engineering that focuses on system-wide questions rather than isolated model-selection issues. The transition to automated systems allows for the handling of millions of traces, enabling the orchestration of repair, review, and evaluation tasks by managed workers. This approach aims to make agent improvement scalable and repeatable, emphasizing the importance of observability, traceability, and reproducibility in these processes.

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