The Discovery Problem: When AI Agents Build Tools No One Can Find
A study found that more than half of newly installed AI skills were invisible to future agents due to missing metadata. The discovery audit showed that all custom-built skills were discoverable, while all external skills were not. The authors argue that invisible capabilities pose a risk in multi-agent systems and recommend self-auditing for discoverability.
- ▪In a single session, fifteen AI skills were installed and all passed functional tests.
- ▪Eight of the fifteen skills (53%) were invisible to future agents because they lacked discovery metadata.
- ▪All seven custom-built skills were discoverable, whereas all eight external skills were invisible.
- ▪The authors propose a discovery audit as a missing step to prevent capability-discoverability gaps in multi-agent toolchains.
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The Discovery Problem: When AI Agents Build Tools No One Can Find We installed 15 AI skills in a single session. Eight were invisible to future agents — not because they were broken, but because they lacked three lines of metadata. Next AI Labs · Human-Agent Research · 9 min read A case study in capability discoverability — the gap between what tools exist in an AI system and what agents can actually find. We document how 53% of newly installed skills failed a discoverability audit, the five surfaces agents use to find tools, and why self-auditing discovery is the missing step in multi-agent toolchains. The most dangerous failure mode in a multi-agent system isn't a tool that breaks. It's a tool that works but can't be found. We discovered this the hard way.
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