Hermes Agent and Jev: pre-LLM hook setup and timings

This tutorial connects Hermes to Jev through a Python pre-LLM hook for routine server checks. The hook classifies a request, runs a predefined command and passes its output to the reasoning model as extra context. Hermes still handles the response; the decision model reduces the work needed to select and call tools.

The setup uses a sponsored Hostinger VPS with two vCPU cores and 8 GB of RAM. The speaker installs Hermes on Ubuntu, chooses a ChatGPT subscription for model access and tests requests through the command line. Jev requires a Typesafe API key in this example because it is a hosted, closed model. The speaker suggests an open decision model hosted through Ollama as another option, but does not demonstrate that route. This is therefore not an offline setup.

The first hook uses Python's standard library to classify disk-usage questions and run df -h. The walkthrough covers hook registration, executable permissions and the Python shebang needed to launch the script. A larger example adds server-health checks and suspicious-log classification with confidence thresholds.

For disk usage, the speaker reports 4.68 seconds with the hook versus 8.4 and 9.2 seconds without it. A log check takes 18.9 seconds with the hook versus 28 seconds without it, though the answers also differ. These examples support using an AI agent hook for known, recurring tasks; they do not establish a general speedup for arbitrary requests.