Knowledge Has No Fixed Home: Dynamic Verified Placement Across External and Parametric Memory

Self-evolving agents must decide whether acquired knowledge should be maintained externally or internalized in model parameters. Neither choice is universally preferable: their value depends on future use, updates, executor costs, and reliability. We formulate *dynamic knowledge placement* and show that preferred representations change with lifecycle conditions, earlier decisions can expire, and average executor success hides knowledge-specific risk. Our method, *Dynamic Verified Placement (DVP)*, filters executors by capability and risk, compares their expected lifecycle value, revisits earlier placements, and verifies new representations before use. On LightShop, external and parametric memory solve complementary tasks. Under fixed budgets, DVP completes 23.78 and 63.32 more correct tasks than always external on the primary and shifted workloads. It also completes 3.80 and 9.38 more tasks than a matched adaptive-amortization policy that uses the same forecasts, executor cards, and transaction protocol but optimizes only serving-time savings. Under the exact benchmark verifier, no incorrect internal output was released.