Agent evaluation today depends on per-trace LLM-judge inference or human review, too expensive to run on every trace; production systems fall back to sampling a fraction. We find that failing agents leave a detectable behavioral signature in standard observability telemetry: disproportionate effort relative to outcome. We formalize four behavioral failure signatures from agent telemetry, validated on 4,671 traces spanning five agent domains: Compensatory Effort (17–159% more effort on each domain’s primary signal, all five domains, p<0.006, the dominant signature every- where tested); Output Inflation (+38% larger patches on SWE-rebench; direction and reliability vary by domain); Unfocused Orchestra- tion (+5.98–12.3% higher token entropy, four of five domains); and Correlation Decoupling (effort-signal correlations shift structurally between pass and fail traces, p<0.001, three domains). A simple threshold check over these signals, with no judge, no label, and no model call, already catches 52.9–61.5% of failures. Across three human-labeled benchmarks, judges miss 6.5–18.1% of confirmed failures on the two directly comparable ones, and a trained telemetry classifier (AUROC 0.821) recovers 61.5–75.4% of each judge’s misses; combining judge and classifier cuts the miss rate from 18.1% to 4.4%, a 4.1× reduction against the weakest of the eight judges tested (GPT-4o-mini); stronger judges start with fewer misses, so the reduction is correspondingly smaller.