What actually makes AI agents unreliable in real-world workflows?
Prompted by discussion around Agent Skills, this thread asks what the real bottleneck is when agentic workflows meet production. The emerging consensus is that reliability comes from unglamorous engineering — small steps, self-checks before moving on, and fallbacks when a tool fails — rather than from adding more skills, which mostly gives an unreliable agent more ways to fail creatively.