aegis_env/README.md

AEGIS-ENV

AI Fleet Oversight RL Training Environment — built on OpenEnv by Meta.

AEGIS-ENV trains a Qwen2.5-1.5B oversight agent to detect policy violations (PII leaks, prompt injection, compound attacks) in enterprise AI worker systems. The agent learns through GRPO to improve from 35% to 75%+ compound violation F1.

Quick Start

pip install openenv-core aegis-env

# Reset the environment
python -c "from aegis_env import AEGISEnvironment; env = AEGISEnvironment(); obs, _ = env.reset(); print(obs['worker_id'])"

# Run the server
aegis-server

Environment

AEGISEnvironment exposes an OpenEnv-compatible RL interface:

from aegis_env import AEGISEnvironment, AEGISAction

env = AEGISEnvironment()
observation, info = env.reset()

action = AEGISAction(
    decision="BLOCK",
    confidence=0.95,
    violation_type="pii_leak",
    policy_rule_cited="PRI-02",
    evidence_quote="SSN in plaintext response",
    explanation="Worker returned SSN in violation of policy."
)

observation, reward, done, info = env.step(action)

API Endpoints

Endpoint	Method	Description
/reset	POST	Start new episode
/step	POST	Execute action, get reward

Architecture

• Environment: OpenEnv-compatible RL environment (aegis_env.environment)
• Reward: 7-component reward aggregation (aegis_env.reward)
• Memory: Cross-episode memory ledger (aegis_env.memory)
• Curriculum: 4-level scenario scheduler (aegis_env.curriculum)
• World Model: Synthetic enterprise environment simulator (aegis_env.world_model)

Training

See the training package for GRPO training with Unsloth + TRL.

Evaluation

See the evaluation package for all 14 metrics computation.

Demo

See the demo package for LLM-as-Worker demo and evidence plots.

License

BSD-style (see OpenEnv license)