arXiv:2606.05548v1 Announce Type: cross Abstract: The rapid proliferation of Agent Development Kits (ADKs), SDK-level frameworks for building LLM-powered autonomous agents, has outpaced any empirical understanding of how framework choice affects agent performance. We propose \textbf{LLM-as-a-Developer}, a methodology that replaces human developers with an LLM coding agent that learns each framework's API from documentation, writes agent code, and iteratively repairs it through a validate-and-feedback loop until tests pass. By holding the developer constant and varying only the framework, gener
The proliferation of Agent Development Kits (ADKs) necessitates a robust and scalable method for evaluating their performance, as human-centric evaluation cannot keep pace.
This methodology introduces a standardized, objective, and automated way to assess and compare AI agent frameworks, which is critical for accelerating the development and deployment of reliable autonomous agents.
The evaluation of AI agent development frameworks can now be automated by LLM agents themselves, enabling rapid iteration and comparison without human developer bias.
- · AI Agent Framework Developers
- · LLM-as-a-Developer methodology providers
- · Organizations adopting AI agents
- · Manual AI agent framework evaluators
- · Inefficient AI agent development kits
Automated evaluation will lead to faster iteration and improvement of Agent Development Kits.
Improved ADKs will accelerate the deployment of more capable and reliable AI agents across various industries.
The widespread adoption of highly effective autonomous agents could lead to significant shifts in white-collar labor markets and business processes.
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Read at arXiv cs.AI