Four engineering patterns to stop AI agents from breaking in production

A software developer building an AI chatbot for a product called Weeve documented four critical failure modes that emerge when agentic AI pipelines move from demos to production environments. These failures include tool call errors, persistent API outages, silent infinite loops, and hallucinated outputs that superficially resemble valid JSON. To address these issues, the developer implemented retry logic with exponential backoff, fallback mechanisms that serve cached data when live APIs are unavailable, loop detection that fingerprints recent tool calls to catch repetitive cycles, and structured observability to log agent steps in a machine-readable format. The article argues that demo environments mask these problems because they rely on clean inputs and stable APIs, while production systems face flaky endpoints, context window overflows, and unmatched exit conditions. The patterns are presented as practical additions to any agentic pipeline rather than full framework replacements.