Agentic prompts grow unbounded, tripling over repository lifetimes

The paper identifies a one-way ratchet: agents accumulate instructions because deletion is risky (undocumented dependencies), but addition is cheap. This isn't just technical debt; it's a structural incentive problem baked into how agentic systems are deployed and maintained.

This connects directly to the self-evolving GUI agents work from earlier this month, which proposed continuous adaptation without human supervision. That framework assumed agents could internalize improvements, but this study reveals the flip side: in practice, deployed agents don't clean up after themselves. They append. The TrustNLP workshop analysis also noted a shift from post-hoc interpretability toward active control of generative systems, but catastrophic remembering suggests that control breaks down once systems start self-modifying in production. The gap between what we want (clean, auditable reasoning paths) and what we get (bloated, opaque instruction files) mirrors the cross-lingual policy retention problem: we measure final outputs while ignoring the actual traces agents leave behind.

If teams using agentic coding systems report that prompt compression or instruction pruning tools gain adoption in the next 6 months, that signals the finding has moved from observation to operational pain. Conversely, if instruction file sizes continue to grow unchecked in public repos through 2027, it suggests the field hasn't internalized the maintenance cost yet.