Your Agents Are Aging Too: Agent Lifespan Engineering for Deployed Systems [R]

Are agents aging after deployment?: https://arxiv.org/abs/2605.26302

On a new longitudinal deployment benchmark, switching the Claude Code CLI agent from Sonnet 4.6 to Opus 4.7 dropped PyTest pass rate by ~15%. This (to me) is a counterintuitive-enough result to pay attention to.

The authors built AgingBench, to measure how coding agents hold up over a long deployment, not just on a single task. On their S7 coding scenario, swapping the backbone model from Sonnet 4.6 to Opus 4.7, within the same Claude Code CLI harness, produced a 15% mean drop in PyTest pass rate across the deployment horizon.

Their argument is that this is a longitudinal effect, not a raw-capability one. The benchmark stresses how an agent's memory state evolves over many sessions (compression, interference, revision, maintenance shocks), and a stronger base model doesn't automatically age better under a given memory policy. In fact, memory policy alone drove a 4.5x spread in agent half-life across scenarios, which is larger than any model swap they tested.

All to say: "newer model, just swap it in" may not be a safe upgrade strategy for long-lived agents.

More details and a runnable benchmark: https://agingbench.github.io

Does this reflect your experience with long-lived agentic deployments?