Choose Your Architecture

Three Ways to Build an AI Auditor

Every AI auditing tool follows one of three architectural patterns. Each has different tradeoffs in speed, depth, and precision. Your choice here shapes everything that follows.

Single-Pass Auditor

How it works: One prompt scans the entire codebase. One pass. One report.

Real examples: Pashov Skills, SCV-Scan, Claude Solidity Skills

Tradeoffs:

• Fast (minutes, low token cost)
• Simple to build and maintain
• Higher false positive rate (no verification step)
• Limited depth (cannot reason across multiple passes)

Best for: Quick feedback during development. Pre-audit scans. CI/CD integration.

Multi-Phase Pipeline

How it works: Structured phases: Reconnaissance → Detection → Verification → Report. Each phase builds on the previous.

Real examples: Krait (4 phases, 100% precision on 50 contests), SolidityGuard (7 phases)

Tradeoffs:

• Better precision (verification phase filters FPs)
• Structured methodology (easier to debug and improve)
• Higher token cost (multiple passes)
• Slower (each phase depends on the previous)

Best for: Thorough audits where precision matters. Tools meant to be trusted.

Multi-Agent System

How it works: Multiple specialized agents analyze the codebase in parallel. Each focuses on a different vulnerability category. Results are merged and deduplicated.

Real examples: SC-Auditor (6 hunt agents), Plamen (18-100 agents), cl0wdit (6 agents)

Tradeoffs:

• Broadest coverage (specialized perspectives)
• Parallel execution (faster than sequential)
• Complex orchestration (deduplication, conflict resolution)
• Expensive (many agents = many tokens)

Best for: Maximum coverage across vulnerability categories. Large codebases.

Pick ONE architecture. This is foundational. It affects which detection strategy fits best (Step 4), how reconnaissance integrates (Step 5), and which verification approach works (Step 7).