Empirical benchmarks using the agent-memory-bench framework reveal that token cost and context window exhaustion, not accuracy, are the primary constraints limiting production agentic AI deployments. Testing three memory strategies against a 30-turn reasoning chain task on GPT-4o (gpt-4o-2024-05-13) at June 2024 pricing ($5/$15 per million input/output tokens) demonstrated that full context retention yields 0.94 accuracy but consumes 19,650 total tokens ($0.1122), nearly triple the cost of alternatives. Selective recall with a 4-turn window reduced tokens to 8,880 ($0.0486) at 0.91 accuracy, while retrieval-augmented external memory (text-embedding-3-small, top_k=4) achieved 7,140 tokens ($0.0434) at 0.84 accuracy. Full recall exhibits near-linear token growth, failing after 20–30 turns due to context limits or cost thresholds, whereas external memory stabilizes per-turn token usage, enabling hundreds of turns. The author argues that leaderboards emphasizing solve rates ignore this operational reality, noting that less than 10% of benchmarks report token costs. A deployment matrix recommends full context only for ≤5 turns, selective recall for 5–25 turns, and external memory/RAG for >25 turns or recurring jobs. The study concludes that hybrid memory architectures are mandatory for cost-viable, long-running agents, urging teams to benchmark cost-per-task as a first-class metric alongside accuracy.