---
title: PM-Ops RL Environment
emoji: 📋
colorFrom: blue
colorTo: purple
sdk: docker
app_port: 8000
pinned: false
tags:
  - openenv
  - reinforcement-learning
---

# PM-Ops: Procedural Project-Management RL Environment

A multi-app simulated environment (Ticketing, Codebase, Chat) featuring **procedural
organizational drift**, designed for reinforcement learning training.

## What makes it RL-shaped

Every episode samples a fresh OrgConfig -- different label names, priority schemes,
team-to-service mappings, and oncall channel names. Frontier LLMs fail because they
rely on memorized conventions; a trained agent learns to read the runbook first.

## Tasks

| Task | Brief | Max Steps |
|------|-------|-----------|
| triage | Bug report -- correct label, priority, team, channel | 25 |
| incident_routing | Alert -- identify owner via commits, page oncall | 25 |
| release_notes | Compile + post release notes in org style | 40 |
| dep_update | Coordinate dependency update across all owning teams | 40 |

## Action space

The agent calls actions like:

  meta.read_runbook       -- learn this org's conventions
  ticketing.create_ticket -- file a ticket with label + priority
  ticketing.assign_ticket -- assign to correct team
  chat.post_message       -- notify the right oncall channel
  meta.finish             -- end episode, trigger scoring

## Reward

Delayed to episode end. Deterministic Python verifier -- no LLM-as-judge.
Partial credit for partial correctness.

## Sponsor alignment

- AI Labs: direct PM-in-the-loop positioning
- Patronus: schema drift -- env tests whether agents track convention changes
- Scale AI: enterprise workflow simulation
- Meta RFC 004: delayed trajectory reward is the native reward mode