Designing Guardrails for Agentic LLM Execution
As agentic LLMs move from answering to acting, permissions, approvals, and safety design matter more than benchmarks.
As agentic LLMs move from answering to acting, permissions, approvals, and safety design matter more than benchmarks.
Home cooking humanoids should be judged by task success, time, safety, and cost, not human-like appearance.
Open-weight LLM safety should be judged not only at release, but by how easily fine-tuning can weaken safeguards later.
A concise look at shielded RL reinterpreted as a design-time tool for structural safety analysis, not runtime blocking.
Model Spec’s chain of command can override custom instructions, causing persona and reasoning drift. Design priorities, exceptions, and fallbacks to improve reproducibility.
SPIRIT uses deep perception uncertainty to gate shared autonomy, switching between semi-autonomous manipulation and haptic teleoperation.
Examines how warmth, memory, and consistency in conversational AI affect intimacy, trust, and safety evaluation criteria.
How small prompt shifts can amplify into risky robot actions, and why alignment alone can’t guarantee physical safety.
In high-risk deployments, prioritize uncertainty, false positives/negatives, and closed-loop failure propagation over single-model scores.
Even with the same model alias, outputs can shift due to snapshot routing, safety behaviors, and sampling settings. Use logs and regression tests to isolate causes.
How to handle relationship-test prompts in AI chats: set refusal boundaries with Safe Complete, document branching rules, and validate via evaluation.
Explores legal investigations into xAI's Grok and the shift toward mandatory AI safety standards in California and the EU.