[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-trm-labs-ai-agents-crypto-investigations-zh":3,"article-related-trm-labs-ai-agents-crypto-investigations-zh":26,"series-blockchain-7ff10146-4ca0-4670-a02c-384dde04f610":69},{"id":4,"slug":5,"title":6,"content":7,"summary":8,"source":9,"source_url":10,"author":11,"image_url":12,"cover_image":12,"category":13,"language":14,"translated_content":11,"related_article_id":15,"keywords":16,"key_takeaways":11,"views":23,"created_at":24,"published_at":25,"topic_cluster_id":11},"7ff10146-4ca0-4670-a02c-384dde04f610","trm-labs-ai-agents-crypto-investigations-zh","TRM Labs 將 AI agent 帶進加密調查","\u003Cp>\u003Ca href=\"https:\u002F\u002Fwww.trmlabs.com\" target=\"_blank\" rel=\"noopener\">TRM Labs\u003C\u002Fa> 這次不是做一個聊天機器人而已。它是把 \u003Ca href=\"\u002Fnews\u002Ftrust-wallet-ai-agents-crypto-trades-zh\">AI\u003C\u002Fa> agent 直接塞進 \u003Ca href=\"https:\u002F\u002Fwww.trmlabs.com\u002Fproducts\u002Ftrm-forensics\" target=\"_blank\" rel=\"noopener\">TRM Forensics\u003C\u002Fa>。講白了，就是讓調查員用白話問問題，系統自己去跑區塊鏈追蹤。\u003C\u002Fp>\u003Cp>TRM 說，去年非法加密貨幣交易量到 \u003Cstrong>1580 億美元\u003C\u002Fstrong>。同時，\u003Ca href=\"\u002Fnews\u002Fai-agents-moving-into-real-work-zh\">AI\u003C\u002Fa> 相關詐騙和騙局也暴增 \u003Cstrong>500%\u003C\u002Fstrong>。這兩個數字放一起看，很直白：犯罪側已經在用自動化，防守方也只能跟上。\u003C\u002Fp>\u003Cp>這種工具不是給一般玩家玩的。它是給執法單位、金融機構，還有加密公司用的。重點不是炫技。重點是把原本很硬的分析流程，縮短成幾個自然語句。\u003C\u002Fp>\u003Ch2>TRM 這次到底上了什麼\u003C\u002Fh2>\u003Cp>TRM 的新 ag\u003Ca href=\"\u002Fnews\u002Frtk-cuts-claude-code-token-spend-zh\">en\u003C\u002Fa>t，核心功能很單純。你可以直接問它資金去了哪裡。也可以問它跨了哪些鏈。還可以問某個錢包群組，是否碰過已知的非法基礎設施。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775058149786-o0ok.png\" alt=\"TRM Labs 將 AI agent 帶進加密調查\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>以前這種事，常常要靠資深分析師手寫查詢。那不是不能做。只是很花時間。尤其當案件一多，手邊又有多條鏈要查時，光是整理問題就會卡住。\u003C\u002Fp>\u003Cp>TRM 的做法，是把自然語言轉成調查動作。這表示調查員不用先學會複雜語法，再去查資料。對實務工作來說，這很省事。真的，這種設計比很多空泛的 AI 口號有用多了。\u003C\u002Fp>\u003Cul>\u003Cli>TRM 說非法加密交易量達 \u003Cstrong>1580 億美元\u003C\u002Fstrong>\u003C\u002Fli>\u003Cli>AI 詐騙與騙局增加 \u003Cstrong>500%\u003C\u002Fstrong>\u003C\u002Fli>\u003Cli>工具先給既有用戶使用\u003C\u002Fli>\u003Cli>目標族群是執法、金融與加密企業\u003C\u002Fli>\u003Cli>查詢方式改成白話提問\u003C\u002Fli>\u003C\u002Ful>\u003Cp>這種產品路線，也反映一個現實。區塊鏈分析工具很強，但學習成本高。你如果要先懂資料結構、節點關係、標記規則，才會問問題，那很多團隊根本用不滿。\u003C\u002Fp>\u003Cp>所以這次不是單純加一個 AI 功能。它是在改工作流。從「先學工具」變成「先問問題」。這差很多。\u003C\u002Fp>\u003Ch2>為什麼現在才上 AI agent\u003C\u002Fh2>\u003Cp>原因很簡單。案件量變多了，分析人力沒那麼快補上。TRM 的說法也很直接：工作量成長速度，比人力快。\u003C\u002Fp>\u003Cp>TRM 的 \u003Ca href=\"https:\u002F\u002Fwww.trmlabs.com\u002Fabout\" target=\"_blank\" rel=\"noopener\">Ari Redbord\u003C\u002Fa> 在說明裡講得很白：調查員要同時處理多條區塊鏈、多個司法管轄區，還有不同類型的犯罪手法。這不是單一工具能硬扛的。\u003C\u002Fp>\u003Cblockquote>“What we’re seeing every day is that the caseload is growing faster than the workforce, and investigators are being asked to operate across dozens of blockchains, jurisdictions, and typologies simultaneously.” — \u003Ca href=\"https:\u002F\u002Fwww.trmlabs.com\u002Fabout\" target=\"_blank\" rel=\"noopener\">Ari Redbord\u003C\u002Fa>\u003C\u002Fblockquote>\u003Cp>這句話很重要。因為它點出 AI 在這裡的定位。不是取代調查員。也不是自動判案。它是把重複查詢、初步追蹤、資料整理這些雜事先做掉。\u003C\u002Fp>\u003Cp>而且現在的犯罪流程也更工業化了。以前可能是一個人搞一個錢包。現在常常是 bot、假帳號、深偽語音、洗錢跳板一起上。你要是還用舊方法追，真的會追到手軟。\u003C\u002Fp>\u003Ch2>跟其他區塊鏈工具比，差在哪\u003C\u002Fh2>\u003Cp>這個市場不是只有 TRM 在玩。\u003Ca href=\"https:\u002F\u002Fwww.chainalysis.com\" target=\"_blank\" rel=\"noopener\">Chainalysis\u003C\u002Fa>、\u003Ca href=\"https:\u002F\u002Fwww.elliptic.co\" target=\"_blank\" rel=\"noopener\">Elliptic\u003C\u002Fa>、\u003Ca href=\"https:\u002F\u002Fwww.ciphertrace.com\" target=\"_blank\" rel=\"noopener\">CipherTrace\u003C\u002Fa> 都在做區塊鏈分析、風險標記、AML 追查這類工作。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775058168940-8msj.png\" alt=\"TRM Labs 將 AI agent 帶進加密調查\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>差別在於，TRM 這次多了一層對話介面。也就是說，你不用先熟悉整套查詢邏輯，先用自然語言說需求就好。對新手來說，這很友善。對老手來說，這是省時間。\u003C\u002Fp>\u003Cp>我覺得這種差異很實際。因為很多分析軟體的問題，不是功能不夠，而是太難用。功能再強，沒人會問，最後還是放著長灰塵。\u003C\u002Fp>\u003Cul>\u003Cli>\u003Ca href=\"https:\u002F\u002Fwww.chainalysis.com\" target=\"_blank\" rel=\"noopener\">Chainalysis\u003C\u002Fa> 偏重監控、合規與調查\u003C\u002Fli>\u003Cli>\u003Ca href=\"https:\u002F\u002Fwww.elliptic.co\" target=\"_blank\" rel=\"noopener\">Elliptic\u003C\u002Fa> 強調風險評分與暴露分析\u003C\u002Fli>\u003Cli>\u003Ca href=\"https:\u002F\u002Fwww.ciphertrace.com\" target=\"_blank\" rel=\"noopener\">CipherTrace\u003C\u002Fa> 長期做 AML 與偵查流程\u003C\u002Fli>\u003Cli>\u003Ca href=\"https:\u002F\u002Fwww.trmlabs.com\u002Fproducts\u002Ftrm-forensics\" target=\"_blank\" rel=\"noopener\">TRM Forensics\u003C\u002Fa> 現在加入 prompt 式調查\u003C\u002Fli>\u003C\u002Ful>\u003Cp>如果把這幾家放一起看，趨勢很清楚。下一輪競爭，不只是誰追錢包最快。還包括誰能讓調查員最快問對問題，然後把答案整理成可用證據。\u003C\u002Fp>\u003Cp>這對公部門很重要。因為人力通常不夠。對交易所和支付業者也重要。因為他們要更快抓出可疑資金流，少漏掉一筆，就少一個大洞。\u003C\u002Fp>\u003Ch2>這對加密執法意味著什麼\u003C\u002Fh2>\u003Cp>先講現實版答案：AI agent 不會自己抓罪犯。它還是可能看錯脈絡，也可能漏掉邊界案例。更麻煩的是，它如果講得很像真的，反而容易誤導人。\u003C\u002Fp>\u003Cp>所以在執法場景裡，人類審核還是不能少。AI 可以幫你縮短前處理時間。它可以幫你整理鏈上路徑。它也可以幫你把調查起點拉出來。但最後下結論的人，還是得是人。\u003C\u002Fp>\u003Cp>不過方向已經很明顯了。調查工具正在變成分析助理，而不是純資料庫介面。這種改法，會讓查詢更快，也會讓分析師把時間放回判斷本身。\u003C\u002Fp>\u003Cp>如果 TRM 的做法跑得順，其他廠商大概也會跟進。到時候，prompt 式調查可能會變成標配。問題只剩一個：誰能把速度和可稽核性一起做好。\u003C\u002Fp>\u003Ch2>這波背後的產業脈絡\u003C\u002Fh2>\u003Cp>區塊鏈分析這個市場，這幾年一直在往兩個方向走。第一個是合規。第二個是執法。前者看風險，後者看證據。兩邊都很吃資料品質，也都很吃流程效率。\u003C\u002Fp>\u003Cp>AI agent 的加入，代表產品介面開始往自然語言靠攏。這跟一般企業軟體的趨勢一樣。大家都想少學幾個指令，多做一點判斷。說穿了，沒人想把時間浪費在記語法。\u003C\u002Fp>\u003Cp>但這裡有個雷點。越是方便的工具，越要保留紀錄。因為調查不是聊天。每一步都可能進報告，甚至進法庭。能不能重現、能不能解釋，會比回答得快不快更重要。\u003C\u002Fp>\u003Ch2>接下來我會看什麼\u003C\u002Fh2>\u003Cp>我會先看兩件事。第一，這個 agent 會不會真的減少分析時間。第二，它產出的結果，能不能維持可追蹤、可驗證、可審計。\u003C\u002Fp>\u003Cp>如果 TRM 做得好，下一步很可能是更多國家執法單位跟進。金融機構也會更想買。畢竟，當犯罪側已經會用 AI，防守側不會只靠人工慢慢查。\u003C\u002Fp>\u003Cp>你可能會想問，這會不會變成所有區塊鏈工具的標配？我覺得很有機會。只是最後活下來的，不會是最會講 AI 的廠商，而是最能把 AI 關進流程裡的那一家。\u003C\u002Fp>","TRM Labs 把 AI agent 放進 TRM Forensics，讓調查員用白話查區塊鏈金流。公司稱去年非法加密交易達 1580 億美元，AI 詐騙也暴增 500%。","www.coindesk.com","https:\u002F\u002Fwww.coindesk.com\u002Fpolicy\u002F2026\u002F03\u002F25\u002Fai-agents-to-help-investigators-unearth-crypto-criminals-according-to-new-trm-program",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775058149786-o0ok.png","blockchain","zh","468d5c05-99ac-4115-ae8e-842755509672",[17,18,19,20,21,22],"TRM Labs","AI agent","區塊鏈分析","加密貨幣調查","TRM Forensics","crypto investigations",8,"2026-04-01T10:33:30.166266+00:00","2026-04-01T10:33:30.127+00:00",{"tags":27,"relatedLang":11,"relatedPosts":32},[28,30],{"name":22,"slug":29},"crypto-investigations",{"name":18,"slug":31},"ai-agent",[33,39,45,51,57,63],{"id":34,"slug":35,"title":36,"cover_image":37,"image_url":37,"created_at":38,"category":13},"9bbb6b03-abcc-4713-bc2f-dd12924b42a2","ai-in-crypto-agents-tokens-use-cases-zh","AI 進入加密：代理、代幣、用例","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782645471800-lko7.png","2026-06-28T11:17:27.597559+00:00",{"id":40,"slug":41,"title":42,"cover_image":43,"image_url":43,"created_at":44,"category":13},"ca9fb038-eb4d-476e-ba2c-c0fbb5b588fc","immutable-x-cuts-nft-game-fees-ethereum-zh","Immutable X 壓低 NFT 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2","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782631072814-gj86.png","2026-06-28T07:17:26.449871+00:00",{"id":58,"slug":59,"title":60,"cover_image":61,"image_url":61,"created_at":62,"category":13},"dd000edb-856b-4450-a0d5-8d2c80125c74","newhedge-bitcoin-live-dashboard-data-zh","5 個指標看懂 Newhedge 的比特幣儀表板","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782622962535-vnhh.png","2026-06-28T05:02:18.259602+00:00",{"id":64,"slug":65,"title":66,"cover_image":67,"image_url":67,"created_at":68,"category":13},"4c16eb0c-dea3-4182-b6c8-ca9dffa20afb","ai-agents-web3-strict-controls-not-hype-zh","AI agents 在 Web3 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行銷怎麼變了","2026-04-02T01:36:34.973322+00:00",{"id":92,"slug":93,"title":94,"created_at":95},"e7992274-42ee-40bc-bb05-97250098c56c","ai-agentic-defi-web3-grants-march-2026-zh","AI、Agentic DeFi 與 Web3 補助案","2026-04-02T05:51:36.857954+00:00",{"id":97,"slug":98,"title":99,"created_at":100},"5cef810b-af3d-467a-8b41-627769eca895","why-crypto-is-fixated-on-ai-agents-zh","為何加密圈盯上 AI Agent","2026-04-02T05:54:28.919864+00:00",{"id":102,"slug":103,"title":104,"created_at":105},"d30e6203-d522-41a1-b529-fcf4499cd985","web3-explained-what-it-is-why-it-matters-zh","Web3 是什麼，為何重要","2026-04-02T06:15:32.580114+00:00",{"id":107,"slug":108,"title":109,"created_at":110},"f29e65ae-64df-463b-ba22-afd9dcbd0f8f","trust-wallet-agent-kit-ai-trade-25-chains-zh","Trust Wallet 讓 AI 幫你交易","2026-04-02T06:27:33.183404+00:00",{"id":112,"slug":113,"title":114,"created_at":115},"91022b4c-b53e-4c18-abfe-914a8eca6e28","blockchain-in-ai-real-use-cases-zh","區塊鏈加 AI，真實落地在哪裡","2026-04-02T06:30:44.026286+00:00"]