Asset or Expense? Keeping the Books of Self-Evolving Agents

Self-evolving agents persist experience as artifacts such as playbooks, memories, or skill libraries, and are evaluated by the accuracy gained while the task stream runs. We ask what the deployer owns when evolution stops. We introduce a ledger that tags every LLM call by phase, content-addresses the artifact before measuring it, and fills four accounts: frozen held-out value, build cost, per-task carry cost, and the lifetime of the backbone the artifact depends on. A recognition gate, fixed before the closed-model runs, refuses to compute depreciation for an artifact whose value is not shown to be positive. Instantiated inside the AgentStream suite with its methods, tasks, and prompts unchanged (54 runs, 8 backbones), no artifact passed the gate; one was significantly harmful after multiple-comparison correction (GPT-5.5's own ACE playbook, −24 points on Tau2-telecom) and two more before correction. On the column with the largest reported online gain (GPT-5.4, Tau2-telecom), both the online gain we reproduce (+0.04) and the frozen value (+0.02, 95% CI [−0.06, +0.11]) are indistinguishable from zero, and on that model a length- and vocabulary-matched, syntax-destroyed copy of the playbook performs comparably. The costs, by contrast, are certain: even with 96% of prompt tokens served from cache, carrying the playbook raises per-task execution cost by 48% and overtakes the one-off build cost after about 67 tasks; online rewriting adds about one cache miss per task. Against the alternative of upgrading to the successor model, the artifact would need at least 7.7 points of value to break even. By these books every artifact we measured is an expense, and we state the four measurable conditions under which one would be an asset. We release the ledger, the gate, and a dataset of dated snapshot lifetimes (12–21 months for 2025 releases).