What really makes AI agents unreliable in real workflows?
A discussion asks whether the real bottleneck for agentic workflows is context and memory, pre-deployment testing, breaking tasks into smaller steps, or recovery from mistakes. The strongest answer is recovery: agents handle the happy path but stall or confidently err when a tool returns something unexpected, so small steps with a check after each one and an explicit stop-and-report rule matter more than a smarter model.