GLM-5.

The constant "babysitting" requirement in current AI coding agents is driving me insane. I’ve spent countless hours tethered to Claude Code and OpenCode, not because they are perfect, but because I was terrified of what would happen if I looked away for even five minutes. It’s a vicious cycle: the agent starts hallucinating or drifting off course, and because of the dopamine hit from seeing the terminal fly by, my ADHD brain refuses to let me break away. I end up stuck in a loop of micro-managing every single file change just to keep the LLM agent from nuking my repository.

I've been testing the latest GLM-5.2 integration, and while it’s far from a perfect solution, the shift in autonomy is noticeable. The real issue with the current state of prompt engineering and agentic workflows isn't just the intelligence of the model—it's the UX. Most of these tools lack the basic transparency needed to build trust. If I can't see a clear, reliable progress bar or a logical breakdown of the agent's intent, I'm going to stay glued to the monitor.

We need to bring actual UX principles back to the AI workflow. When an agent is running a complex deployment or a refactor, I shouldn't feel like I'm watching a live bomb fuse. I need to know exactly what stage of the reasoning process it's in.

The "Babysitting" Problem in Agentic Workflows

The technical bottleneck isn't just about the context window; it's about the reliability of the tool-use loop. Here is why I feel "chained" to these models:

• Unpredictable Execution: You trigger a command, and you have no idea if it's going to resolve in 10 seconds or 10 minutes, or if it's currently stuck in a recursive loop.
• The Feedback Gap: Most CLI-based agents provide a wall of text that is impossible to parse visually while doing other work.
• Lack of Guardrails: Without a way to visually confirm the "plan" before the "execution" phase, you're essentially gambling with your codebase.

Moving toward autonomous deployment

I'm looking for a real-world way to implement a more robust AI workflow where the model can actually handle a task from scratch without me hovering. We need better visibility into the agent's internal state. A "complete guide" to using these agents shouldn't just be about the right prompts; it should be about setting up a sandbox where the agent can fail safely and report back clearly.

If we can move toward a system where the UX provides a high-level summary of the agent's trajectory—similar to how a CI/CD pipeline shows stage progression—we might finally be able to treat LLMs as teammates rather than unruly interns that need constant supervision. GLM-5.2 is a step in that direction, but the industry still has a massive UX debt to pay off.