Future-Proofing Agent Supervision
summary
EffiSciences' BELLs project curates a GitHub corpus of labeled traces (jailbreaks, prompt injections, hallucinations), keeps some classes and examples private for evaluation, and runs third‑party scoring against submitted detectors; it pairs manual red‑teaming with automated benchmarks and a unified trace format. Two concrete metrics measure accuracy on held-out anomaly and early-warning proficiency for spotting precursors to harmful actions; practical focus is on input-output safeguards that stack in series or parallel and generalize to out‑of‑distribution attacks.
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chapters (9)
- 0:00Agent supervision challenges
- 1:51LLM application failures
- 5:41LLM system failures taxonomy
- 7:47Input-output safeguards role
- 11:24Safeguard evaluation metrics
- 14:13Third-party safeguard evaluation
- 21:57Open collaboration and future plans
- 22:52Q&A
- 27:50Future autonomous agent threats
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