Agentic AI systems are increasingly proposed for social-good domains, often invoking the United Nations Sustainable Development Goals (SDGs). Yet such claims do not necessarily establish accountability to the communities these systems aim to serve. We present a structured survey of 112 papers on agentic AI for social good published between 2015 and 2026. Each paper is coded across eight dimensions using dual-LLM annotation (GPT-4o and Llama-3.3-70B) with stratified human validation on 50 papers.

We identify a moral-geographic asymmetry: contextual underspecification is concentrated where local context matters most. Overall, 82 of 112 papers (73%) specify no geographic context. Papers aligned with health or physical/ecological SDGs name a geography 37–40% of the time, compared with only 13% for institutional and social-policy SDGs, and 0% under strict coding that requires a concrete country, city, region, or population (𝜒²(2)=8.78, p=.012, Cramér’s V=.283; strict: 𝜒²(2)=7.60, p=.022, V=.263). The pattern is robust across seven of eight sensitivity analyses. SDG 16 is both the most-covered goal and the least geographically specified.

We interpret this pattern as consistent with a form of moral abstraction: institutional good is framed more universally despite its dependence on local political, legal, and cultural conditions. Only 28 papers (25%) report any real-world deployment or small-scale test. We detect no statistically significant differences in deployment rates across architecture, era, or geographic specificity. We conclude with five accountability gaps and a minimal reporting standard for more context-specific, participatory, and accountable research.