Self-reflective agents typically bundle monitoring language with a downstream control policy, making it difficult to identify why reflection changes behavior. We present a six-condition ablation that separates evidence re-exposure, diagnostic questions, uncertainty labels, differentiated follow-up actions, and diagnosis–action alignment. Our testbed is an LLM forecasting agent evaluated with Llama-3.3-70B on 310 armed-conflict escalation cases, plus supplementary GPT-4o and 12-country transfer probes. Diagnostic questions produce comparable F1 to free reflection (0.296 vs. 0.297), and adding a seven-label taxonomy while holding the action fixed changes F1 by only +0.008. The closest matched comparison for action differentiation is taxonomy-only versus random action assignment (F vs. D), which holds the typed monitor and taxonomy fixed while replacing the shared-action policy with randomly varying substantive actions: F1 rises from 0.304 to 0.338. Type-matched routing reaches 0.379, but we detect no significant advantage over random assignment (C vs. D, p=0.714). This underpowered contrast does not establish equivalence; it prevents assigning the gain specifically to matching. Brier scores remain flat and transfer is unreliable. Thus, richer reflective traces do not establish better agent control. Our results motivate controls matched for information, vocabulary, and actions when attributing gains in reflection pipelines and identify simple action perturbations as a necessary baseline for type-specific policies.

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.