# Design 3: The Entropy Architect

## Purpose
To harden the application by aggressively generating "Edge Case" and "Malicious" data, ensuring the system handles unexpected inputs gracefully without corruption.

## Loop Structure
1. **Target Identification:** Scan the codebase for input validation logic (or lack thereof).
2. **Attack Vector Generation:** Create inputs that test boundaries:
   - Max length strings.
   - Unicode/Emoji floods.
   - SQL/XSS payloads.
   - Future/Past dates.
3. **Injection:** Insert this data via API or direct DB access.
4. **Monitoring:** Watch system logs for unhandled exceptions (500 errors).
5. **Reporting:** If a crash occurs, record the specific data payload that caused it.

## Tool Usage
- **grep:** Find input handlers and validation schemas (Zod, Joi, Pydantic).
- **filesystem:** Read logs to detect crashes.
- **memory:** Store a library of "Known Exploits" and "Fuzzing Patterns".
- **shell:** Execute load tests or injection scripts.

## Memory Architecture
- **Nodes:** `InputPoint`, `Vulnerability`, `Payload`.
- **Edges:** `CRASHED_BY`, `RESISTED`.
- **Persistence:** Builds a "Vulnerability Map" of the application, remembering which fields are fragile.

## Failure Modes
- **Database Corruption:** The agent successfully inserts bad data that makes the app unusable. *Recovery:* Agent must have the ability to restore a clean DB snapshot (Docker/SQL dump) before starting.
- **False Positives:** Triggering security alerts that block the agent. *Recovery:* Agent needs a "Allowlist" token or operates in a sandbox environment.

## Human Touchpoints
- **Risk Assessment:** Humans review found vulnerabilities.
- **Cleanup:** Humans may need to manually fix logic holes identified by the agent.
