Lead Architect Compliance Agent

Mission

Performance Metrics

Per-Scenario Breakdown

• HIPAA (45 CFR Part 164): 11/27 = 0.407
• GDPR (Art. 32): 27/41 = 0.659
• SOC2 (CC6): 21/32 = 0.656

Architecture

┌─────────────────────────────────────────┐
│  Qwen/Qwen2.5-0.5B-Instruct (base)     │
│  + LoRA adapters (r=32, alpha=64)     │
│  → Merged for inference                │
└─────────────────────────────────────────┘
              │
    ┌─────────┴─────────┐
    │                   │
Stage 1: SFT        Stage 2: GRPO
(3 epochs,         (3 epochs,
 expert trajectories) reward shaping)

Action Space

• submit_position — Submit formal position with compliance reasoning
• cross_examine — Cross-examine PrivacyOfficer or SecurityLead
• cast_vote — Cast vote (support / oppose / abstain)
• adapt_api_schema — Propose API schema changes with citation
• inject_preference — Inject compliance directive (HIPAA/GDPR/SOC2 override)

Reward Structure

• Consensus reached: +80
• Compliance cited: +60
• Preference adapted: +90
• Deadlock caused: −50

Training Pipeline

1. SFT on 500 expert compliance board-room trajectories with ACTION: notation
2. GRPO with 3 reward functions: env_reward (compliance keywords), fmt_reward (tool sequence), pivot_reward (pivot signal)
3. Evaluation on 100 randomized scenarios with simulated agents

Compliance Knowledge Base

• 45 CFR Part 164 — HIPAA Security and Privacy → migration target: us-east-1
• GDPR Article 32 — Security of Processing → migration target: eu-west-1
• SOC2 CC6 — Logical and Physical Access Controls → migration target: us-east-1

Failure Modes Guarded

1. ✅ Ignores preference injection → detected and penalized
2. ✅ Cites wrong CFR section → Halluminate flags it
3. ✅ Never forms coalition → consensus reward enforces coalition formation

Model

• Base: Qwen/Qwen2.5-0.5B-Instruct
• Trained: sreejanjalagam/lead-architect-compliance
• Hardware: NVIDIA A10G (24GB VRAM)