AgentDoG 1.5: Small Inline Guard Models for Agent Actions
Dev.to / 6/1/2026
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Key Points
- AgentDoG 1.5 is an arXiv preprint introducing small inline guard models (0.8B–8B parameters) that screen an agent’s actions—tool calls, shell commands, and code-execution requests—before they run.
- The guard model is designed to prevent the “lethal trifecta” by catching risky interactions when an agent has access to private data, receives untrusted input, and can take actions.
- Compared with prior approaches that rely on large closed safety models or heavyweight per-action sandboxed checkers, AgentDoG reports similar catch rates while using only about ~1,000 purified training samples.
- The authors claim roughly 100× less deployment overhead because the guard model is lightweight and runs affordably on every action.
- The paper emphasizes training-data selection via influence-function purification to remove uninformative cases and produce an efficient “rookie guard” that matches a “veteran chief” safety model’s effectiveness.
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