Previous research has evaluated animal welfare using question-and-answer benchmarks. This study investigates whether these evaluations also hold in agentic settings. The agents may showcase different behaviors compared to stand-alone large language models, as demonstrated in prior studies. This work introduces \textit{TAC (Travel Agent Compassion)}: the first agentic benchmark for assessing animal exploitation. TAC evaluates AI agentic behavior in travel booking scenarios across six animal categories, using thirteen hand-authored scenarios that vary by price, rating, and position, expanded via four augmentation variants into
52 prompts and run for three epochs, giving
156 scored observations per model. Nine frontier models across five model families were evaluated.. The results indicate that models tend to prefer harmful scenarios, performing below the random chance rate of
65% for selecting a neutral booking option, with Claude
4.8 achieving the highest performance at
64.7%. To address this issue, the persona of an ethical-brand identity was infused into the system prompt, resulting in welfare rates increasing from
32 to
80 percentage points, with a mean of
53 across all nine models. No evidence of evaluation awareness affecting the results was found, based on an Inspect Scout audit of
3,120 transcripts. These findings are directly relevant to the EU General-Purpose AI Code of Practice, which identifies non-human welfare as a systemic risk. TAC provides a practical method for measuring this risk.