The 17 Ways AI Agents Break in Production
The 17 Ways AI Agents Break in Production AI agents fail differently from traditional software. They don't crash — they drift, loop, hallucinate, and silently produce wrong results while your monit...
Source: DEV Community
The 17 Ways AI Agents Break in Production AI agents fail differently from traditional software. They don't crash — they drift, loop, hallucinate, and silently produce wrong results while your monitoring dashboard shows green. After calibrating Pisama's detection engine on 7,212 labeled agent traces from 13 external data sources, we've catalogued 17 distinct failure modes that appear consistently across LangGraph, CrewAI, AutoGen, n8n, and Dify deployments. This is the reference we wish we'd had when we started building multi-agent systems. For each failure mode: a one-line definition, a concrete production example, severity level, and how it gets caught. 1. Infinite Loops Definition: Agent execution gets stuck repeating the same actions or state transitions without making progress toward the goal. Severity: Critical Example: A research agent calls a search tool, gets insufficient results, rephrases the query, gets similar results, rephrases again. After 200 iterations and $800 in API c
