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.