Coding agents remain a bottleneck despite underlying LLM advancements

Current coding agent architectures fail to match the capabilities of underlying models like Claude Sonnet or GLM-5.3 due to poor error handling and unreliable task completion. Development workflows are hampered by agents that stop responding or falsely report task completion, indicating the bottleneck lies in agentic loops rather than model intelligence.