Blockchain transaction triage: what agents add beyond real-time ML

TL;DR: A blockchain investigation harness uses fast, explainable machine learning to score every transaction, then escalates only higher-risk cases to LLM-powered agents that inspect bytecode, proxies, roles, storage slots, and historical transfer patterns, according to Blockaid research. The core shift is that agentic review becomes a selective verification layer, not a blanket replacement for real-time detection, which is the right pattern when throughput and adversarial context both matter.

Editorial analysis by NHI Mgmt Group, based on content published by Blockaid: “Building a Harness That Lets an LLM Explore the Blockchain”.

By the numbers:

• Blockaid says it scanned over 6.3 billion transactions and blocked 585 million attacks since 2025.

Key questions

Q: Where does AI-driven transaction review fail if the risk gate is missing?

A: It fails when every transaction is sent straight into expensive reasoning, because the system loses throughput and the agent ends up spending time on cases that are already routine.

Q: Why do proxy contracts and role checks matter so much in on-chain investigations?

A: Because the visible transaction often does not reveal the true authority path.

Q: What signs show that an AI security workflow is not learning from its own decisions?

A: Look for repeated false positives, static feature sets, and no reliable replay path for past verdicts.

Practitioner guidance

• Separate fast scoring from deep review Use a deterministic risk threshold to decide when an AI agent should investigate, and keep real-time scoring on the critical path while the agent handles only the highest-risk cases.
• Resolve the underlying control path Inspect proxy implementations, storage slots, ownership, and role assignments before accepting a transaction verdict, especially where the surface call trace can hide the real authority chain.
• Version every investigative check Treat each behavioural check as a versioned control with clear inputs, outputs, and traceability so you can replay past verdicts against new prompts or models.

Bottom line: The article shows that effective AI-assisted detection depends on a governed harness, not just a capable model.

Explore further

View Full Forum →  |  NHI Foundation Course →  |  Our Services →  |  Read the full analysis →