arXiv:2607.03691v1 Announce Type: cross Abstract: Coding agents, autonomous systems that use large language models (LLMs) to resolve software engineering tasks, rely on agentic scaffolding: a middleware layer in between a developer and a large language model that orchestrates system prompts, tool execution, context management, and iterative reasoning loops. While these scaffoldings evolve at extreme velocities, no study has examined how this evolution affects agent quality (i.e., effectiveness and efficiency) over time. Practitioners regularly report quality regressions after scaffolding updat
The rapid development and deployment of coding agents are reaching a point where the underlying stability and quality of their foundational scaffolding are becoming critical issues.
Understanding the impact of scaffolding evolution on coding agent quality is crucial for robust AI software development and preventing regressions in autonomous systems.
The focus in AI agent development will broaden from solely LLM capabilities to include the design, maintenance, and impact of agentic scaffolding itself.
- · Companies specializing in AI agent orchestration platforms
- · Developers skilled in debugging and optimizing complex AI agent systems
- · AI quality assurance services
- · Developers solely focused on LLM fine-tuning without understanding agentic syste
- · Companies with brittle or poorly managed agentic scaffolding
- · Early adopters of rapidly evolving, unstable agent frameworks
Increased emphasis on the robustness and versioning of AI agent middleware layers.
New standards and best practices will emerge for managing and updating agentic scaffolding to ensure quality.
The development of AI agents capable of autonomously managing and optimizing their own scaffolding.
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Read at arXiv cs.AI