Five Common AI Agent Failures and How Developers Can Prevent Them

AI agents that perform well during testing often break in unexpected ways once deployed in real environments. A recurring set of failure modes includes infinite loops, context window overflow, and tool name errors that cause silent or hard crashes. Developers can guard against runaway loops by setting hard iteration limits and defining clear completion criteria in code. Context bloat can be managed through sliding window strategies or token-aware trimming that preserves critical system prompts. Catching tool name mismatches early with strict validation prevents agents from silently failing or endlessly retrying invalid calls.