When Do Brain-Inspired Components Improve Agent Memory?

Long-term agent memory increasingly uses brain-inspired storage, forgetting, reinforcement, and retrieval, yet system-level gains leave the advantage of each mechanism unclear. Removing a component tests system dependence; it does not establish superiority over a functioning alternative. We formulate component attribution as a comparison defined by the replacement, measured endpoint, and state or resource constraint. Our matched-substitution protocol applies this formulation to storage substrates, memory dynamics, and retrieval routing, checks mechanism activation, and separates shared-state readout contrasts from closed-loop comparisons that allow retrieval feedback. These controls change what the observations support. Hybrid sparse distributed memory lowers answer accuracy by about 14.8 percentage points relative to dense storage on frozen synthetic streams, while favorable readout cells disappear against compact equal-byte dense controls. Temporal decay helps in tested regimes with an advancing clock and distinguishable trace states, whereas joint reinforcement can revive stale rules. Chronological answer replay leaves the joint benefit of dense readout and restricted reinforcement unresolved. Prospective graph retrieval improves bridge-question F1 by 9.24 points under a read constraint; a separate lifecycle study measures substantial construction and maintenance costs. The resulting design guidance is conditional: compare compact storage alternatives, test when temporal updates affect ranking, and evaluate routing quality alongside cost. Reliable attribution requires functioning substitutions within a stated operating regime, beyond removal effects alone.