VQV Signal
Researchers at Infobip propose a four-phase workflow to structure human interaction with LLM-based coding agents, improving reliability in complex coding tasks. The approach combines a foundation model with a behavior-shaping harness to guide agent-assisted development.
Researchers at Infobip propose a four-phase workflow to structure human interaction with LLM-based coding agents, improving reliability in complex coding tasks. The approach combines a foundation model with a behavior-shaping harness to guide agent-assisted development.
AI-assisted summary based on the listed source.
LLM-based coding agents combine a foundation model with a harness that shapes agent behavior. For non-trivial tasks, how practitioners structure their work with the coding agents determines whether reliable results follow. We report on a phased workflow for operating coding agents developed by the AI research team...
Effective workflows are crucial for leveraging AI coding agents in non-trivial tasks, ensuring dependable outputs. This structured method highlights the importance of human effort in managing AI tools for software development.
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VQV surfaced this signal because it is recent, relevant to AI Coding Tools, connected to arXiv.
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