Robots navigating human-populated environments must avoid collisions while respecting the social structure of crowds, particularly the implicit boundaries of social groups. Most navigation approaches model humans as independent individuals,causing socially disruptive behavior even when collision-free. This paper presents TAGA (Tangent Action for Group Avoidance), detected group boundaries via tangent-path maneuvers without modifying the underlying navigation policy. A hierarchical safety controller coordinates group-level avoidance with individual collision prevention. We propose the Group Crossing Rate (GCR), a continuous metric measuring the fraction of timesteps the robot spends inside any group convex hull, providing finer-grained social compliance assessment than terminal metrics alone. We introduce a realistic crowd simulation benchmark with five empirically grounded phases: individual speed heterogeneity, group speed coupling, F-formation static groups, leader-follower dynamics, and convex-hull boundaries, evaluated under both ORCA and Social Force pedestrian dynamics. Experiments across ORCA, Social Force, DS-RNN, and Intention-RL reveal a reactive-learning asymmetry: TAGA provides the largest gains for classical reactive baselines (up to +8pp success rate, GCR halved) with near-zero cost for learned policies. These findings offer actionable guidance for when modular group-awareness adds value versus when end-to-end group-aware training is preferable.