Capgemini-在公共部门构建人工智能代理（英）

Architecting AI agentsin the public sectorA detailed guide to game-changing multi-agent platforms for technical leadsWhat’s insideIntroduction 01Why AI agents matter 02Supercharging processes 02Language, data and context: the keys to automation 03What is an AI agent? 04The anatomy of an AI agent 05The evolution of AI: levels of autonomy in agentic AI 07Multi-agent architectures 09Collaboration in agent spaces across organizations 10On-premise vs. cloud 12 Platform overview: automation meets agent intelligence 13This is what AI really looks like Real-world uses cases in the public sectorAutomating social media posts with ChatGPT and Zapier 15Handling citizen emails 16Automating meter readings via WhatsApp 17Automating services with Relevance AI 18Monitoring and dashboards 2114Critical reflection and limitation2223 From agentic vision to action Six steps public sector leaders should take now27Summary and outlook What we have learnt from the rise of AI agentsIntroductionThe public sector is facing a significant skilled labor shortage, which is worsening due to ongoing demographic shifts. To address this, increasing automation is essential, enabling public organizations to handle tasks more efficiently with fewer people.AI agents sound futuristic – and they are. Think of fully automated processes and intelligent assistants that solve problems on their own. That is exactly what AI agents bring to the table. So why should you care? Because AI agents take your automation to a whole new level.Automation without AI is like using a typewriter in the age of computers; it is better than handwriting, but it misses out on the transformative potential and efficiency that modern technology offers.Modern automation platforms provide a strong foundation: you build workflows, transfer data, and automate repetitive tasks. But the real transformation happens when you integrate AI agents into those workflows – agents that analyze data, make decisions, and optimize processes in real time with a level of precision that is impossible to achieve manually.Recent research by the Capgemini Research Institute suggests that public sector organizations are awake to this potential: 90% of those surveyed plan to implement agentic AI in the next 2-3 years.1 But the field is evolving rapidly. The concept of agentic AI is still emerging and often complex. Many solutions are experimental, fragmented, or deeply technical – which makes orientation difficult. That is why we focus on clarity in this point of view: We illustrate typical architectures, compare core technologies, and show how AI agents can work in practice, across sectors and scenarios.While there is a growing ecosystem of commercial platforms – from open source to enterprise-grade – our goal here is to show what is possible and not to describe single solution