Skill bundles

Define and enforce hard constraints on what agents can do during training rollouts

Systematically find ways an agent could game the reward function

Maintain and switch between agent versions when new RL training degrades performance

Monitor deployed RL-trained agents for performance drift, reward hacking in the wild, and distribution shift

Set up recurring RL training loops that retrain as the workflow or data distribution shifts

Quantify the business impact (time saved, error reduction, cost) of RL-trained agents

Build reliable, fast environment reset mechanisms for episode boundaries

Verify that the sandbox environment faithfully reproduces production behavior

Ingest, clean, and transform client data into RL-ready formats

Build a faithful replica of a client's production system (APIs, DB, auth) for safe RL training

Evaluate whether the client has sufficient trajectory data, or whether collection needs to happen first

Work with stakeholders to convert vague "it should work better" into measurable, scorable outcomes