編者按：目前 AI Agents 在各行各業的應用前景廣闊，越來越多的企業開始嘗試部署 AI Agents ，然而如何在企業生產環境中有效部署和管理 AI Agents，是工程師們面臨的一大挑戰。你是否曾面臨這樣的困惑：如何確保 AI Agents 在生產環境中穩定可靠地運行？如何應對突發的高并發請求？當 AI Agents 出現"幻覺"或系統崩潰時，又該如何快速恢復？

本文提出了 "Multi-Agent-as-a-Service(MAaaS)" 這一概念，認為可以借鑒云應用的最佳實踐（best-practices）來設計、部署和管理 AI Agents 系統。MAaaS 框架涵蓋了從系統架構到監控管理的各個方面，包括明確的職責劃分、RESTful 接口設計、獨立的數據存儲、容器化部署、自動化測試等關鍵策略。

我們相信，隨著對 MAaaS 概念的不斷探索和完善，Multi-Agent 系統有望在各個行業中引發變革，為 AI 應用開辟新的道路。

作者 ?? | Sam Rajaei & Guanyi Li

編譯 ?? | 岳揚

關于 AI Agents（能夠基于特定指令和對上下文的理解獨立完成任務的自主系統[1]）的討論[2]一直不絕于耳。其討論熱度幾乎可與大語言模型（LLMs）相媲美。本文將從一線工程師、系統架構師以及平臺可靠性工程師（SREs）的視角出發，探討 AI Agents，介紹 Multi-Agents-as-a-Service 這一概念，以及他們如何在未來的生產環境中與 AI Agents “打交道”。

01 Context：AI Agents 能解決什么問題？

在探討 AI Agents 能解決的問題時，我們發現它們在以下任務場景表現出色 —— 模仿人類交流方式可以更有效執行的任務，比如需要理解自然語言、情感識別、個性化服務或者需要與人類用戶進行互動的任務。

1. 在電子商務領域，借助 LLM-based RAG 或 Text-to-SQL 等技術， AI Agents 能夠根據公司規定準確回答用戶的問題，從而為客戶提供更加個性化的購物體驗，有望顛覆傳統的電商模式。
2. 客戶服務領域也是 AI Agents 的理想應用場景。很多人都有過為了一些簡單的問題（比如訂單狀態沒有更新）長時間排隊等待客服的經歷。一些初創公司（例如 Decagon[3]）正通過 AI Agents 來解決這些效率低下的問題。
3. 也十分適合根據個人需求提供產品或內容的定制化領域，Wix[4] 就是一個典型案例。面向無需編寫代碼或只需編寫少量代碼的網站搭建需求，Wix 開發了一個聊天機器人，它能夠通過問答與用戶互動，根據用戶的描述和需求來搭建網站。

“人類只需設定目標（goals），而 AI Agents 會獨立決策，選擇達到目標所需的最優行為規劃。”[5]

基于 LLMs 的 AI Agents 在模擬人類的自然對話和執行簡單的商業流程操作等方面表現較好，往往能夠提供既高效又令人滿意的服務。

02 工程師們關于 AI Agents & Enterprise Production Environments 的觀點

在思考了 AI Agents 的諸多優勢后，你是否曾好奇過它們如何在企業生產環境中運行？什么樣的架構模式和基礎設施組件能夠最好地支持它們的正常運行？當遇到不可避免的錯誤，如 Agents 出現幻覺[6]、系統崩潰，或（可能更加糟糕）在執行重要任務時進行了錯誤的推理和規劃，我們該怎么辦？

如果我們是一名工程師，必須審慎思考這些問題。更重要的是，我們還需探討一個根本性的問題：我們該如何界定 Multi-Agent 平臺首次部署成功的標準？

為了尋找答案，我們可以借鑒軟件工程領域的另一個分支——可靠性工程（Reliability Engineering）中的一個概念：服務質量目標（Service Level Objectives, SLOs）。SLOs 是衡量服務性能與可靠性的關鍵指標，簡單來說，SLOs 規定了“成功”操作與總操作的 acceptable ratio（譯者注：可以接受的或合理的比例，如果這個比例高于某個預先設定的閾值，那么服務的性能就被認為是可接受的。反之，如果低于這個閾值，則可能需要采取優化措施。），以及這些操作對用戶體驗的影響。這些目標將幫助我們設定 AI Agents 及其支持的工作流所需達到的服務要求和預期水平。

那么，SLO 與我們討論的 AI Agents 有什么關系呢？

我們在討論 AI Agents 的性能時，可以將其簡化為兩個核心目標 —— “可用性（Availability）”和“準確性（Accuracy）”，并且識別出一些更具體的、有助于實現這些目標的 SLOs ：

1. 可用性：描述的是 Agents 或平臺對請求（requests）給出成功響應（例如 HTTP 200 狀態碼）的比例。從以往來看，底層服務器的正常運行時間和 ping 命令的成功率是衡量可用性的重要指標。然而，在微服務架構盛行的今天，這種傳統的衡量方式可能變得不那么重要了。現在的平臺系統更傾向于通過響應用戶請求成功的次數與響應用戶請求失敗的次數來準確反映系統的可用性。此外，延遲（Latency）[7]和吞吐量（Throughput）也是與之相關的指標。
2. 準確性：這一點與 AI Agents 快速且穩定地響應用戶的請求不同，它更側重于 Agents 在無人驗證其工作準確性的情況下，能否從業務角度正確執行任務并返回準確數據。傳統系統也會監測數據準確性和質量的 SLOs。

衡量上述 AI Agents 的性能目標是否達到時，通常需要實時地收集和提交應用程序內部的各種性能指標數據，可以按照固定的時間間隔（比如每10分鐘一次）收集、提交，或者是對特定事件（如用戶請求、上游調用等）做出響應時收集、提交。例如，可以用 Synthetic[8] probing（譯者注：通過模擬或合成的方式來探測系統，通常是為了測試系統在處理現實情況時的性能。例如，在測試一個網站或服務器的響應時間時，可以用它來模擬用戶發起的請求，然后測量服務器對這些請求的響應時間和成功率。） 來模擬用戶請求，觸發相關事件，并監控相關數據。此處我們要探討的關鍵問題是：那些設計相對簡單、行為可預測的傳統系統，其輸出總是確定的、可預測的，因此，對其進行監控、探測和評估通常比較直接。但對于復雜多變的 GenAI Agents，情況卻未必如此。

請注意，本文主要關注兩個目標中的第一個——可用性。包括確定驗收標準，這些驗收標準確保系統在運行時能夠保持穩定，不受外部因素的影響，以便幫助 Agents 順利處理用戶的 query 。如果想要更深入了解準確性（例如為 AI Agents 設定合理的任務范圍、優化 few-shot 方法和評估框架的性能）的相關內容，這篇文章[9]可作為入門學習資料。

現在，回到工程師在部署 AI Agents 時為確保基礎架構的可靠性而需要做好的事情。為了達成服務質量目標（SLOs）并打造一個可靠而安全的平臺，工程師應當重點關注以下幾個因素：

• Scalability：當請求量突然增加時，系統能否有效應對？
• Cost-Effectiveness：大語言模型（LLM）的使用成本高昂，如何監控和控制成本？
• High Availability：如何確保系統始終可用且響應迅速？AI Agents 是否具備自我修復功能，并能夠從錯誤（errors）或系統崩潰（crashes）中恢復正常？
• Security：如何確保數據在靜態存儲過程中和傳輸過程中的安全??，如何完成安全審計、漏洞挖掘等工作？
• Compliance & Regulatory：這是人工智能領域的一個重大議題，我們必須遵守哪些相關的數據隱私法規和其他特定行業標準？
• Observability：如何實時監控 AI Agents 的業務流程、運行狀態和資源利用率，以便在問題影響用戶體驗之前識別并解決？

是不是聽起來很耳熟？似乎 AI Agents 與 Web 應用、微服務架構和云基礎設施所面臨的挑戰非常相似。

那么，現在該怎么辦？我們提出了一個用于 AI Agents 開發和維護的框架，該框架遵循了多年來在一系列工程和軟件學科中形成的最佳實踐（best-practices）。

03 Multi-Agent-as-a-Service (MAaaS)

這次，讓我們借鑒云應用領域（cloud-based applications）的最佳實踐，重新思考如何在生產系統中設計 AI Agents：

• 明確的職責范圍：每個 Agents 都應該有一個定義明確且職責范圍較小的工作領域，并明確其功能界限。通過這種模塊化方法，AI Agents 能夠更準確的執行任務，且更易于管理和不依賴其他組件獨立擴展。
• RESTful 接口風格和服務間使用異步通信方式：使用 RESTful API 進行用戶與 AI Agents 之間的通信，并利用消息代理（message brokers）進行異步通信。這種方法讓 AI Agents 之間的交互更加松散，從而提高了系統的可擴展性（scalability）和容錯能力（fault tolerance）。
• 每個 AI Agent的數據獨立存儲**：每個 Agent 都應該有自己的數據存儲空間，每個 Agent 的數據應該是封閉的，不會與其他 Agent 的數據混淆或混合。必要時使用分布式數據存儲解決方案，使每個 Agent 更加獨立和易于管理。
• 使用容器化技術和容器編排來管理和部署應用程序：使用容器（如 Docker ）來打包和部署 AI Agents，確保它們在不同的環境中保持一致性，簡化了 Agents 的部署（deployment）和擴展（scaling）（譯者注：即增加或減少 Agents 實例的數量）過程。借助容器編排平臺（如 Kubernetes ）來管理 Agents 服務的整個生命周期，包括部署、擴展和日常運維。
• 自動化測試和 CI/CD 流程：實施自動化測試（包括單元測試（unit）、集成測試（integration）、契約測試（contract）和端到端測試（end-to-end）），以確保對 AI Agents 進行可靠的代碼變更管理。使用 CI 工具在代碼提交時自動構建和測試 AI Aegnts 。建立 CD pipelines，無縫地將更新后的代碼（changes）部署到生產環境，減少停機時間并確保快速迭代。
• 可觀測性（Observability）：為 AI Agents 及其支持的基礎設施實施強大的可觀測工具，如 metrics（譯者注：可量化的指標，用于衡量系統性能、資源使用情況、響應時間等。）、tracing（譯者注：跟蹤和記錄系統內部操作的詳細信息，包括請求如何從一個服務流向另一個服務等信息。）和 logging（譯者注：通常記錄錯誤、警告和信息性消息，以便在出現問題時進行回溯和分析），以建立一個能夠實時顯示平臺可靠性的工具或界面。計算每個 Agent 及其處理的所有請求流的 SLOs 和 error budgets（譯者注：在 SLOs 中允許的錯誤次數或錯誤率）。通過模擬用戶請求和高效地處理系統警告和故障，可以在 Agents 產生的問題對大量終端用戶造成影響之前及時發現并解決。

通過應用前文提到的這些原則，我們就可以構建一個強大的、可靠的 AI Agents 框架，并提出 “Multi-Agent as a Service”（MAaaS） 這一概念。這種方法利用了云應用的最佳實踐，重新定義了 AI Agents 的設計、部署和管理方式。

AI Agents 能夠在業務運營中扮演著比較關鍵的角色。然而，Agents 需要一個強大的基礎設施支撐著它的運行，不能獨立于這些環境之外，確保它能夠滿足生產環境中的預期需求，基礎設施中的關鍵組件包括：

• SOA 架構（Service-Oriented Architecture）：在設計 AI Agents 時，將其設計成可以輕松集成到現有系統中的 services（譯者注：可以獨立運行，也可以作為更大的系統或應用程序的一部分，可以選擇使用云服務商的云服務，按需使用。）。
• API 網關：使用 API 網關來管理和保護客戶端與 Agents 之間的流量。
• 彈性基礎設施：利用可以根據需求彈性擴展或縮減資源的云基礎設施。
• 云服務提供商托管的云服務：使用云服務提供商托管的服務（如數據庫服務、向量存儲服務、消息傳遞服務和機器學習服務）來減少運維成本。
• 集中監測（Centralized Monitoring）：使用集中監測解決方案（如CloudWatch、Prometheus、Grafana）來追蹤 AI Agents 的運行狀況和性能情況。

為了突出前文所提 Multi-Agent 系統的優點，我們將展示一個簡單的 Multi-Agent 系統示例：一個辯論平臺。

04 Example: Multi-Agent 辯論模擬系統

為了生動演示 MAaaS 的運作方式，我們打造了一個 Multi-Agent 辯論模擬系統。辯論的主題是“AI 對就業市場的影響（AI’s impact on the job market）”。這個辯論系統包含三個 Agent：

• 支持 AI 對就業有益的 A 隊
• 持有相反觀點的 B 隊
• 負責管理辯論過程的主持人，辯論將在八輪之后或兩隊之間的討論開始變得沒有太多意義時結束。

我們使用 PhiData[10] 來構建 AI Agents ，并通過 AWS Elastic Kubernetes Service（EKS） 進行部署，以實現高可用性。Agent 的活動通過 AWS CloudWatch 進行監控，EKS 的 service discovery 功能（譯者注：分布式系統中自動發現服務的機制，用于簡化服務的發現和交互過程。）可確保 Agents 之間的通信順暢。Agents 之間的對話歷史記錄被存儲在數據庫中，以便在出現故障時，任何備用的 Agents 都能無縫地繼續討論。這種遇到故障及時恢復的彈性通過一個消息隊列得到了增強，只有當消息被消費者完全處理并確認后，消息隊列才會將這個消息標記為已處理，從而確保消息不會丟失或重復處理。為了保持對話的流暢性，每個 AI Agents 目前被限制為只有一個實例運行，盡管 Kubernetes 能夠確保在 Pod 下線時系統仍然運行正常。

為了方便用戶在本地環境中試用該系統，我們編寫了一個在 MiniKube 環境中部署該系統的 YAML 文件。在這個簡化版本的系統中，我們省略了 postgres 數據庫，每個 Agents 都暫時將其對話歷史記錄存儲在內存中。這一調整使得該系統更加輕便，更適合本地部署，同時還保留了其核心功能。你需要首先在操作系統上安裝 MiniKube[11]、Ollama[12] 和 kubectl[13]。

將上述內容保存到名為 deploy.ymland 的文件中，然后運行：

開始辯論（minikube 在 Linux 系統和 Windows 系統上的使用方式略有不同）：

獲取辯論記錄：

刪除系統資源：

這些 Agents 接著進行了一場精彩的辯論（見文末附錄中的辯論記錄）。

05 Conclusion

對 Multi-Agent 系統的興趣為創新和提高效率帶來了無限可能。通過應用云原生的一些原則和最佳實踐，我們可以構建可擴展、成本效益高、安全且高可用性的 Multi-Agent 系統。MAaaS模式不僅與現代軟件工程原則相契合，還為更復雜且適合生產環境的人工智能應用開辟了道路。 隨著我們對這些概念的不斷探索和完善，Multi-Agent 系統在各個行業中引發變革的可能性變得越來越大。

Appendix: Debate Output

debate_team_a states

Thank you, honorable moderator! As Team A, I'm thrilled to present our argument that Artificial Intelligence (AI) positively impacts the job market.

Firstly, AI creates new job categories and industries. According to a McKinsey report, by 2030, AI could create around 140 million jobs globally, which is roughly equivalent to the entire workforce of the United States today. New roles like AI developer, data scientist, and machine learning engineer have already emerged, offering exciting opportunities for individuals to upskill and reskill.

Secondly, AI augmentation enhances existing jobs by automating repetitive and mundane tasks, freeing humans to focus on high-value activities that require creativity, empathy, and problem-solving skills. A study by the World Economic Forum found that 75% of employees in the future will need to develop skills such as complex problem-solving, emotional intelligence, and creativity.

Thirdly, AI increases productivity and drives economic growth. According to a report by Accenture, organizations that adopt AI can expect an average increase of 13.7% in profitability. This is because AI enables businesses to make data-driven decisions, optimize processes, and improve customer experiences.

For instance, the healthcare industry has seen significant improvements with AI-powered diagnostic tools and personalized medicine. A study by Deloitte found that AI adoption in healthcare can lead to a 10% reduction in treatment costs and a 15% increase in patient satisfaction.

Lastly, AI fosters innovation and encourages entrepreneurship. Startups like Zoom, Slack, and Airbnb have revolutionized industries with AI-powered solutions. According to a report by CB Insights, AI has been a key driver of startup success, with over 50% of unicorns (startups valued at $1 billion or more) incorporating AI into their business models.

In conclusion, AI is not a job-killer but a job-enhancer that creates new opportunities, enhances existing roles, drives productivity, and fosters innovation. By embracing AI, we can unlock the potential for economic growth, job creation, and a brighter future for all. Thank you!

debate_team_b states

Thank you, honorable moderator! As Team B, I'm here to present our argument that Artificial Intelligence (AI) negatively impacts the job market.

While it's true that AI creates some new job categories, we must acknowledge that these roles often require specialized skills and are not accessible to everyone. Moreover, many existing jobs are at risk of being automated, leaving workers without a safety net. A report by the International Labour Organization found that up to 20% of the global workforce is at high risk of automation.

Furthermore, AI's tendency to automate repetitive tasks can lead to job displacement and unemployment. According to a study by the University of Oxford, nearly half of all US jobs are at risk of being automated by the 2030s. This will disproportionately affect low-skilled workers, exacerbating income inequality.

Additionally, AI's impact on productivity growth is overstated. A report by the Federal Reserve Bank of San Francisco found that while AI may lead to short-term gains in productivity, its long-term effects are more nuanced and may even lead to decreased productivity as workers adapt to new job requirements.

To illustrate our point, let's look at the retail industry. While some jobs have been created in e-commerce logistics and customer service, many traditional retail roles have disappeared due to AI-powered automation. A study by the National Retail Federation found that 75% of retailers reported an increase in online shopping during the pandemic, leading to store closures and job losses.

In conclusion, while AI has its benefits, we must acknowledge its darker side: job displacement, increased income inequality, and economic disruption. As we move forward with AI adoption, it's crucial that we prioritize upskilling programs, retraining initiatives, and social safety nets to support workers who will be affected by this technological shift. Thank you!

debate_team_a states

Honorable moderator, thank you for the opportunity to present our argument in favor of Artificial Intelligence (AI) positively impacting the job market.

While it's true that AI may displace some jobs, we must recognize its potential to create new ones and augment existing roles. According to a report by Gartner, AI is expected to create over 500,000 new jobs by 2022, primarily in areas like data science, machine learning engineering, and AI development.

Furthermore, AI's automation capabilities can actually enhance productivity and efficiency, freeing up workers to focus on higher-value tasks that require human skills. A study by the McKinsey Global Institute found that AI could boost global GDP by 1.2% to 2.8% by 2030, primarily due to increased productivity.

Moreover, AI's impact on job displacement is often overstated. While some jobs may be automated, many new roles will emerge in industries like healthcare, finance, and education, which require human skills and empathy. A report by the World Economic Forum predicts that by 2025, 75% of employees in emerging technologies like AI, robotics, and blockchain will need reskilling or upskilling.

To illustrate our point, let's look at the banking industry. While some jobs have been automated due to AI-powered chatbots and robo-advisors, many new roles have emerged in areas like data science, risk management, and cybersecurity. A study by Deloitte found that 77% of financial institutions reported increased productivity and efficiency due to AI adoption.

In conclusion, while AI presents challenges, its potential benefits far outweigh the drawbacks. By investing in education and training programs, we can ensure workers are equipped to take advantage of the new job opportunities AI creates. Furthermore, policymakers must prioritize social safety nets and retraining initiatives to support those affected by technological change. Thank you!

debate_team_b states

Honorable moderator, I'd like to respectfully disagree with my opponent's assertion that AI positively impacts the job market. In fact, I firmly believe that AI leads to job displacement, increases economic inequality, and causes economic disruption.

Firstly, while AI may create new jobs in areas like data science and machine learning engineering, it will also displace many existing jobs that are routine, repetitive, or can be automated. According to a report by the McKinsey Global Institute, up to 800 million jobs could be lost worldwide due to automation by 2030.

Secondly, AI's impact on job displacement is not limited to just low-skilled workers. Many middle-class jobs will also be at risk of being replaced or significantly altered, exacerbating economic inequality. A study by the Economic Policy Institute found that between 2009 and 2017, wage growth for the top 10% of earners in the US increased by 24%, while wages for the bottom 90% grew by only 5%.

Thirdly, AI's automation capabilities can indeed boost productivity, but they also risk causing economic disruption. As machines take over routine tasks, businesses may struggle to adapt and innovate, leading to job losses and business failures. A report by the International Monetary Fund found that up to 10% of small and medium-sized enterprises (SMEs) in developed economies could disappear due to automation.

Finally, let's not forget about the human impact of AI-driven job displacement. According to a study by the Oxford Martin Programme on Technology and Employment, up to 47% of US workers are at high risk of being replaced by machines. This will lead to significant social and economic disruption, particularly for those who are already disadvantaged or marginalized.

To illustrate our point, let's look at the retail industry. While some jobs have been automated due to AI-powered chatbots and self-checkout lanes, many more will be displaced as e-commerce continues to grow. A study by the National Retail Federation found that up to 10% of US retail workers could lose their jobs due to automation.

In conclusion, while AI may create new job opportunities, its potential risks far outweigh its benefits. It is crucial that policymakers prioritize social safety nets and retraining initiatives to support those affected by technological change. We must also invest in education and training programs that prepare workers for the rapidly changing job market. Thank you!

debate_team_a states

I understand your concerns about AI's impact on the job market, but I strongly disagree with the notion that AI is a net negative for employment. In fact, I firmly believe that AI has the potential to create new job opportunities, improve efficiency, and foster innovation.

Firstly, while it's true that AI may displace some jobs, it will also create entirely new ones that we can't yet imagine. According to a report by Gartner, by 2025, artificial intelligence will create more than 500,000 new jobs in the United States alone. Additionally, many existing jobs will be augmented by AI, making them more efficient and productive.

Secondly, rather than exacerbating economic inequality, AI has the potential to help bridge the gap between the rich and the poor. A study by the World Economic Forum found that 65% of workers believe that AI will increase job opportunities for people with lower-skilled jobs. Furthermore, AI can help small businesses grow and thrive, creating new opportunities for entrepreneurs and employees.

Thirdly, while it's true that AI may cause some economic disruption, history has shown that technological advancements have always led to new opportunities and growth. A report by the International Institute for Applied Systems Analysis found that automation could lead to a 10% increase in GDP growth rate in developed economies by 2030.

Finally, let's not forget about the human impact of AI-driven job displacement. Rather than being a negative force, AI can be harnessed to create more meaningful and fulfilling work for humans. According to a study by Deloitte, 67% of workers believe that AI will enable them to focus on higher-value tasks that require human skills like creativity, empathy, and problem-solving.

To illustrate the positive impact of AI, let's look at the healthcare industry. AI-powered diagnostic tools are already being used to improve patient outcomes and reduce costs. A study by the American Medical Association found that AI could help reduce hospital readmissions by 20% and lower treatment costs by 15%.

In conclusion, while AI may pose some challenges for the job market, its potential benefits far outweigh its drawbacks. By investing in education and training programs, social safety nets, and retraining initiatives, we can ensure that workers are prepared to thrive in an AI-driven economy. Thank you!

debate_team_b states

I understand your points and appreciate the data you've provided to support them. However, I'd like to respectfully disagree with some of your assertions.

Firstly, while it's true that AI may create new jobs, I'm concerned about the pace at which these new opportunities are being created versus the rate at which existing jobs are being displaced. A report by the McKinsey Global Institute found that up to 800 million jobs could be lost worldwide due to automation by 2030. This is a significant concern, as many of those jobs may not be replaced in the same numbers.

Secondly, regarding economic inequality, while AI has the potential to create opportunities for lower-skilled workers, I'm worried about the widening gap between the haves and have-nots. A study by the International Monetary Fund found that the top 1% of earners in the United States have seen their share of national income grow from 11% in 1989 to over 20% today.

Thirdly, while AI-driven innovation can lead to economic growth, I'm concerned about the uneven distribution of these benefits. A report by the Organization for Economic Cooperation and Development found that between 2008 and 2017, the top 10% of earners in the United States saw their real income grow by over 20%, while the bottom 90% experienced little to no growth.

Finally, I understand your point about AI enabling humans to focus on higher-value tasks. However, many workers may not have the skills or resources to adapt to these new roles. A study by the World Economic Forum found that up to 75% of employees will need significant retraining by 2025 due to automation.

To illustrate the challenges posed by AI, let's consider the example of the manufacturing industry. While AI has improved efficiency and productivity in some sectors, it has also led to widespread job losses and changes in the nature of work. A report by the McKinsey Global Institute found that up to 40% of current manufacturing jobs could be automated.

In conclusion, while AI does have the potential to create new opportunities and improve efficiency, I believe we need to carefully consider its impact on the job market, economic inequality, and social disruption. We must invest in education and training programs, as well as social safety nets and retraining initiatives, to ensure that workers are prepared for an AI-driven economy. Thank you!

debate_team_a states

I understand your concerns about the potential downsides of AI on the job market, economic inequality, and social disruption. You've presented some compelling data and examples to support your points.

However, I'd like to offer a more balanced perspective. While it's true that AI may displace some jobs, history has shown us that new technologies have always created as many or more jobs than they've destroyed. According to the World Bank, the rise of automation in the 19th century led to job losses in agriculture and manual labor, but it also enabled the creation of new industries like manufacturing and services.

Regarding economic inequality, I agree that AI has exacerbated existing issues. However, I believe that targeted policies, such as progressive taxation, social safety nets, and investment in education and training programs, can help mitigate these effects.

In terms of uneven distribution of benefits, I acknowledge that AI-driven innovation has disproportionately benefited those who are already well-off. However, this is not unique to AI – many technological advancements have followed a similar pattern. What's important is that we recognize the issue and implement policies to address it.

Regarding your point about workers needing significant retraining by 2025, I agree that upskilling and reskilling will be crucial for many employees. This presents an opportunity for governments and companies to invest in education and training programs, as well as provide support for workers who may need assistance adapting to new roles.

To illustrate the opportunities presented by AI, let's consider the example of healthcare. AI has the potential to improve patient outcomes, reduce costs, and enhance the quality of life for millions of people around the world. According to a report by Accenture, AI could generate up to $150 billion in annual value for the US healthcare system.

In conclusion, while I acknowledge your concerns about the challenges posed by AI, I believe that with careful planning, investment in education and training, and targeted policies, we can create an AI-driven economy that benefits all workers, regardless of their background or skill level. Thank you!

debate_team_b states

I appreciate your thoughtful response and willingness to engage in a nuanced discussion about the impact of AI on the job market. You're right; history has shown us that technological advancements have often led to new job creation, and it's essential to consider this broader context.

However, I'd like to clarify that while some jobs may be created, AI-driven automation is uniquely capable of replacing tasks that were previously performed by humans. According to a study by the McKinsey Global Institute, up to 800 million jobs could be lost worldwide due to automation by 2030. This is not just a matter of job replacement but also concerns the type and quality of jobs being created.

Regarding economic inequality, I agree that targeted policies can help mitigate some of the effects. However, we must acknowledge that AI-driven innovation has been disproportionately benefiting those who are already well-off. The wealthy are more likely to own assets that benefit from AI-driven investment, such as stocks or real estate, and they have greater access to education and training programs.

Regarding your point about healthcare, I agree that AI can bring significant benefits to patient outcomes, costs, and quality of life. However, we must consider the broader societal implications of AI adoption. For instance, AI-powered job displacement could lead to increased poverty rates, decreased social mobility, and heightened social tensions.

Ultimately, while I acknowledge your optimism about the potential for AI-driven innovation, I believe it's crucial to address the challenges and risks associated with widespread AI adoption. We must prioritize a human-centered approach that focuses on retraining workers, providing support for those displaced by automation, and ensuring that the benefits of AI are shared equitably across society.

Let's continue this discussion to explore ways to mitigate the negative impacts of AI while harnessing its potential to create a more equitable and prosperous future.

host response: Decision: End

Reason: The current message from Team B presents a comprehensive and nuanced response that addresses the concerns raised by Team A, providing concrete examples and statistics to support their arguments. While there are still some points left to be addressed, Team B has effectively countered most of Team A's key points, and the debate appears to have reached a natural conclusion. Additionally, since the current round is already at 8, it seems fitting to bring the debate to an end.

Winning Decision: Team A

Reason: While both teams presented strong arguments, Team A effectively addressed the potential drawbacks of AI adoption by offering concrete solutions to mitigate its negative impacts on job markets and economic inequality. Their proposals for targeted policies and education/training programs demonstrated a deeper understanding of the complexities surrounding AI's integration into society. In contrast, Team B focused primarily on highlighting the benefits of AI-driven innovation without adequately addressing the concerns about job displacement and social disruption.

Thanks for reading!

Hope you have enjoyed and learned new things from this blog!

Sam Rajaei

Engineering VP, Founder, AI/ML Author, Lifelong learner | Newsletter: ??https://www.linkedin.com/newsletters/last-week-in-genai-7140321867323596800/??

Guanyi Li

??https://www.linkedin.com/in/guanyi-li-91662016/??

END

??文中鏈接??

[1]??https://medium.com/binome/designing-llm-based-agents-key-principles-part-1-7e8c6fe3ddaf??

[2]??https://www.deeplearning.ai/the-batch/issue-245/??

[3]??https://decagon.ai/??

[4]??https://www.wix.com/??

[5]??https://aws.amazon.com/what-is/ai-agents/??

[6]??https://www.ibm.com/topics/ai-hallucinations??

[7]??https://sre.google/workbook/implementing-slos/#:~:text=A latency SLO can capture,for different types of services??.

[8]??https://www.dynatrace.com/news/blog/what-is-synthetic-testing/??

[9]??https://blog.langchain.dev/planning-for-agents/??

[10]??https://www.phidata.com/??

[11]??https://minikube.sigs.k8s.io/docs/start/?arch=%2Fmacos%2Farm64%2Fstable%2Fbinary+download??

[12]??https://ollama.com/download??

[13]??https://kubernetes.io/docs/tasks/tools/#kubectl??

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原文鏈接：

??https://towardsdatascience.com/multi-agent-as-a-service-a-senior-engineers-overview-fc759f5bbcfa??