How to Drive an LLM (home.robusta.dev)

A recent innovation in coding agent technology was demonstrated when an engineer from a startup quickly deployed a complex new feature using the Claude Code framework. Within the span of a conference call, ten autonomous agent instances were launched on separate Kubernetes clusters, collaborating seamlessly to implement remote tool calling. This significant leap was made possible due to a sophisticated "harness" that allows the agent to autonomously set up and test infrastructures, troubleshoot failures, and validate outcomes in real-time, removing the need for human intervention during the process. This achievement is crucial for the AI/ML community, particularly for startups aiming to compete against larger companies, where development speed—termed "velocity"—is critical. The ability for coding agents to independently identify problems and refine their solutions in a sandboxed environment accelerates development cycles exponentially. By minimizing human involvement, these agents can operate continuously, significantly enhancing operational efficiency. The author emphasizes starting small by enabling agents to diagnose their own errors, thereby gradually empowering them to manage more complex tasks without human bottlenecks, paving the way for more autonomous AI development processes.