agent_submission.md

To submit **a new agent** for evaluation, developers should only need to:

1. Adhere to Standardized I/O Format: Ensure the agent run file complies with the benchmark-specific I/O format. Depending on HAL's implementation, this could involve:
   * Providing a specific entry point to the agent (e.g., a Python script or function)
   * Correctly handling instructions and the submission process. For example, in METR's Vivaria, this can mean supplying a *main.py* file as the entry point and managing *instructions.txt *and *submission.txt *files.

2. Integrate logging by wrapping all LLM API calls to report cost, latency, and relevant parameters. 
   * For our own evaluations, we have been relying on [Weights & Biases' Weave](https://wandb.github.io/weave/) which provides integrations for a number of LLM providers. 
   * Both, [Vivaria](https://github.com/METR/vivaria) and UK AISI's [Inspect](https://github.com/UKGovernmentBEIS/inspect_ai) provide logging functionalities. 
   * However, there are some missing pieces we are interested in such as latency and parameters of LLM calls. Weave provides a minimum-effort solution.

3. Use our CLI to run evaluations and upload the results. The same CLI can also be used to rerun existing agent-benchmark pairs from the leaderboard.