LLM-based autonomous agents fail in ways that existing observability infrastructure cannot detect. OpenTelemetry's GenAI semantic conventions cover LLM invocation and tool execution but leave five critical agent orchestration phases—planning, reasoning, safety monitoring, inter-agent delegation, and memory management—without span-level representation. We present \textsc{AgentTelemetry}, an open-source benchmark suite and toolkit for evaluating fault detection in agent systems. The benchmark defines (1)~a taxonomy of 14~fault types mapped to 9~agent-specific span kinds, (2)~a controlled evaluation harness of 490 fault-detection cells (14~faults $\times$ 5~observability conditions $\times$ 7~frameworks; enumerated as 2{,}940 raw configurations across 6 mock-LLM seeds), and (3)~a pip-installable library (3{,}700+ LOC, 78~tests) with adapters for seven frameworks. On the controlled benchmark, the full span taxonomy achieves a Fault Detection Rate (FDR) of~1.000—an upper bound confirming structural completeness—compared to 0.429 for vanilla OpenTelemetry and OTel+GenAI. An ablation study proves all nine span kinds are necessary: removing any one makes at least one fault type undetectable. A case study on 112~SWE-bench Lite instances reveals that 84/112 agent runs (75%) exhausted the 8-iteration limit and are classified as reasoning loops by structural pattern (a definitional partition of the failed-trace population, not a sampling estimate)—a failure mode invisible to vanilla OTel—and a telemetry-guided intervention improves the patch rate by +12.5~pp over a matched control (Fisher's exact $p=0.53$, two-sided; demonstrative not statistically significant at $n{=}24$). All code, data, and benchmark configurations are open-source for reproducibility.