arXiv:2604.00392v2 Announce Type: replace-cross Abstract: Agents that synthesize their own tools ship a second artifact alongside each answer: a software library that future tasks reuse, compose, and depend on. Task completion (TC) certifies the answer; it does not certify the library. On a Claude Haiku 4.5 pilot, we patch the harness to preserve per-tool source and replay every synthesised tool against a held-out conformance suite. Across 222 preserved tools and three protocols, 96.8% record per-tool correctness C=0.00: two protocols silent-rot at 100%, one at 91.7%. Hand-written reference im
The proliferation of tool-evolving agents highlights a critical gap in current evaluation methods, as models increasingly self-modify their operational libraries.
This research reveals that successful task completion does not guarantee the integrity and reliability of the underlying agent-synthesized code, posing significant risks for future AI systems.
The focus must shift beyond mere task completion to rigorous verification and conformance testing for AI-synthesized tools, impacting the deployment and trustworthiness of autonomous agents.
- · AI verification & validation firms
- · Security researchers
- · High-assurance AI developers
- · Developers solely focused on task completion metrics
- · Early adopters of unverified agentic systems
- · AI systems prone to silent code rot
AI agent development will require more robust testing and verification methodologies, increasing development complexity.
The public and regulatory bodies will demand higher standards of auditability and trustworthiness for autonomous AI, slowing deployment for critical applications.
This could lead to a new sub-industry dedicated to the automated verification and self-healing of AI-generated code, becoming a prerequisite for scaled AI agent adoption.
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Read at arXiv cs.AI