The Reality Check
A March 2025 reentrancy exploit drained $47 million from a DeFi protocol in 90 seconds. Three separate audit firms had reviewed the contract. All three missed it.
AI agents designed for smart contract auditing would have caught it, according to benchmarks against 200 real-world exploits. The same tools also generate false positives at three times the rate of experienced humans. This is the trade-off enterprise teams need to understand as blockchain deployments scale.
What Actually Works
The effective systems combine three layers: traditional static analysis (AST parsing, control flow graphs), LLM reasoning trained on 10,000+ audited contracts, and verification engines that generate concrete exploits to prove findings. The verification step separates serious tooling from code review chatbots.
Against the Smart Contract Weakness Classification registry's 174 known vulnerability types, AI agents detected 84% in 35 minutes. Human auditors caught 78% in five days. The false positive rates tell a different story: 12% for AI, 8% for humans. The combination, 94% detection at 4% false positives, suggests the technology's actual role.
The Economics Shift
Traditional audits cost $50,000 to $500,000 per protocol with 6-12 month backlogs. AI agents handle pre-scoping (dependency mapping, hotspot flagging), fuzzing, invariant testing, and instant regression after fixes. This frees human auditors for logic analysis, governance review, and adversarial simulation.
The industry lost $3.8 billion to smart contract exploits in 2024-2025. Top audit firms still miss 15-30% of critical vulnerabilities. Continuous monitoring via AI agents caught 93% of historical exploits within 10 minutes, versus 67% for pre-deployment audits.
What to Watch
The pattern mirrors enterprise security tooling evolution: automated scanning finds known issues, humans hunt for novel attacks. Teams like Trail of Bits and OpenZeppelin are building agent architectures that reason about economic invariants and cross-contract interactions, not just pattern matching.
The technology works best where DeFi complexity exceeds human review capacity. It fails where novel logic requires adversarial imagination. That split defines the next phase of blockchain security infrastructure.