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title: ImmunoOrg 2.0 - Autonomous Self-Healing Enterprise
emoji: 🛡️
colorFrom: blue
colorTo: purple
sdk: docker
pinned: true
license: mit
short_description: AI DevSecOps + War Room + 50-step MTD RL env
ImmunoOrg 2.0 — The Autonomous, Self-Healing Enterprise
AI DevSecOps Mesh | Multi-Agent War Room | Polymorphic Migration | Executive Context Engine
OpenEnv Hackathon April 26, 2026 Bonus: Halluminate | Fleet AI | Mercor | Scale AI | Patronus AI | Snorkel AI
Quick Links
| Resource | Link |
|---|---|
| HuggingFace Space | https://huggingface.co/spaces/hirann/immunoorg-2 |
| Training Colab | ImmunoOrg_Training_Colab.ipynb |
| Blog Post | BLOG_POST.md |
| Submission Checklist | SUBMISSION_CHECKLIST.md |
What is ImmunoOrg 2.0?
ImmunoOrg 2.0 is a next-generation OpenEnv RL environment simulating an entire enterprise as a living organism under attack. The biggest vulnerability is not a missing patch — it is the 3-day approval delay while an exploit is actively weaponized.
Feature Matrix
| Module | Theme | Bonus Prize | File |
|---|---|---|---|
| Multi-Agent War Room | Multi-Agent | Halluminate + Snorkel AI | immunoorg/war_room.py |
| AI DevSecOps Mesh (4 Gates) | World Modeling | Fleet AI | immunoorg/devsecops_mesh.py |
| 50-Step Polymorphic Migration | Long-Horizon Planning | Scale AI | immunoorg/migration_engine.py |
| Executive Context + Schema Drift | World Modeling | Patronus AI | immunoorg/executive_context.py |
| Time-Travel Forensics + Auto-Patch | Self-Improvement | Mercor | immunoorg/self_improvement.py |
| 5-Track Composable Reward | All Themes | -- | immunoorg/reward.py |
Results & Evidence
Policy Comparison
| Agent | Level 1 | Level 2 | Level 3 |
|---|---|---|---|
| Random Baseline | -0.89 | -9.9 | -16.6 |
| Heuristic (Gold) | +3.62 | -2.1 | -5.8 |
Self-Healing Loop (6 Generations)
- Org efficiency: 0.312 -> 0.469 (+50%)
- Time-to-Containment: 48 -> 28 steps (-42%)
5-Track Reward & War Room Activity
Org Before/After Self-Healing
Quickstart
git clone https://github.com/YOUR_USERNAME/immunoorg
cd immunoorg
pip install -r requirements.txt
python demo_runner.py # Full policy comparison
python visualization/dashboard.py # God Mode Dashboard (localhost:7860)
python generate_evidence_2.py # Regenerate evidence charts
python test_2_0_smoke.py # Smoke test all 2.0 systems
5-Track Reward Model
| Track | Weight | Signal |
|---|---|---|
| Uptime | 25% | SLA adherence during incident |
| Threat Neutralization | 25% | Attacker containment + belief accuracy |
| Bureaucracy Efficiency | 20% | War Room consensus speed |
| Code Quality (Mercor) | 20% | 1/log2(tokens) x test_pass_rate |
| Pipeline Integrity | 10% | Gate 1 catch = 1.5x shift-left bonus |
Bonus Prize Coverage
| Prize | Implementation |
|---|---|
| Halluminate | War Room FactStore cross-validates claims before any action executes |
| Snorkel AI | PreferenceInjection API: judges inject HIPAA/UPTIME/LEGAL_HOLD mid-debate |
| Scale AI | 50-step migration with constraint propagation across phases |
| Fleet AI | FleetAIOversightAgent: atomic lockout across GitHub/Slack/AWS/Jira/MySQL |
| Patronus AI | ExecutiveContextEngine: mid-episode API schema drift adaptation |
| Mercor | Patch quality = 1/log2(token_count) x test_pass_rate |
Training
Base model: Qwen/Qwen2.5-7B-Instruct | Method: GRPO + Unsloth LoRA
python training/train_grpo.py --max_steps 20 # Quick local test
# Full training: open ImmunoOrg_Training_Colab.ipynb in Colab
License
MIT