The rise of fully autonomous agents necessitates a rigorous evaluation of their reliability under **real-world API complexity**. Accordingly, moving beyond perfectly **sanitized** settings that disregard realistic obstacles, we introduce ***WildAGTEval***, a configurable benchmark environment that accounts for two critical dimensions of realistic API complexity: **API specification**, which includes detailed documentation and usage constraints, and **API execution**, which captures runtime challenges. Consequently, ***WildAGTEval*** offers (i) an API system spanning 86 functions across 7 domains with 60 independently activatable complexity scenarios, yielding approximately 32K distinct test configurations, and (ii) 300 multi-turn conversations grounded in this system. Through systematic evaluation of 11 state-of-the-art LLMs, we find that: (1) individual complexities reduce accuracy by up to 23.1%; (2) compounding them amplifies this drop to 52.1%; and crucially, (3) failing agents fabricate success and distort intent rather than admitting limitations, exposing a critical reliability gap invisible to existing benchmarks.