We put file, network and secret policies outside agents, then replay the same sandbox configuration locally and in the cloud before rollout.

We test snapshot resume and parallel forks on a KVM host, then measure startup latency, isolation failures and S3-compatible persistence.

We can assess whether an AI tool needs an editable canvas instead of a conversation, especially when the task requires tangible artefacts.

We store session traces as JSONL, review corrections before publishing them, and expose only approved repository conventions through MCP.

We can map a media pipeline into explicit artefacts such as scripts, timings, storyboards, briefs and source code before delegating tasks.

We benchmark one model before and after quantisation, pruning or distillation, then validate its TensorRT-LLM, SGLang and vLLM exports.