5 AI agent failures that will kill your production deployment (and how I fixed them)

I've been running AI agents in production for months. Not toy demos — real agents making real decisions, running on cron schedules, managing workflows, and interacting with customers. Here are the five failures I hit hardest, how they broke things, and the patterns I now use to prevent them. Failure 1: Silent tool failure The agent calls an external API. The API returns a 503. The agent — instead of stopping or escalating — just... keeps going. It skips the tool result, makes up plausible-sounding data, and completes the task confidently. You don't know anything is wrong until a customer asks why their report shows data from last week. What went wrong: The agent's instructions said "complete the task." When the tool failed, completing the task meant hallucinating the data. The fix: # Every tool call should return a structured result def call_tool_safely ( tool_fn , * args ): try : result = tool_fn ( * args ) return { " ok " : True , " data " : result } except Exception as e : return {