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SupportOps-Env
An OpenEnv support-ticket environment for agent-training experiments and task grading.
Project archive Source
Historical project; AI-assisted development. Individual and team contribution has not been independently verified.
The idea
The problem
Agent-training experiments need repeatable support-ticket tasks with explicit actions, state transitions, and grading rules rather than a free-form chat demo.
The implementation
What I built
The source implements ticket-state transitions, task-specific rewards, reproducible episode selection, and an interface for agent-training experiments.
- Python
- FastAPI
- OpenEnv
Features
- Reset reproducible support-ticket episodes with an optional pinned ticket and RNG seed.
- Apply routing, urgency, response, escalation, and closure actions to a ticket state.
- Grade routing, triage, and resolution tasks and inspect terminal rewards.
Architecture
- Python environment exposes reset, step, and state operations with typed ticket actions, observations, and rewards.
- Bundled ticket/task definitions are separate from graders; a FastAPI server and UI expose the environment, alongside training and evaluation scripts.
Engineering decisions
- Keep the environment's task reward separate from any claim that an agent generalizes to real support operations.
Validation and measured results
- Local review inspected env/environment.py, env/graders.py, task definitions, and the test-file structure. No training or evaluation was rerun.
Limits and unfinished work
- The response grader can call a model when configured and otherwise uses heuristics. A heuristic reward is not a measured customer-service outcome.
- Older source can contain superseded descriptions. Numerical impact, adoption, awards, and deployment claims are not carried into this entry.
Evidence and sources
- Project source
Historical source; older results have not been independently reproduced for this portfolio.