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capgemini-在公共部门构建人工智能代理

信息技术2025-11-25Capgemini章***

AI智能总结

AI agents in the public sector

AI agents are intelligent software systems that autonomously perceive their environment, reason about it, and act to achieve specific goals. They combine decision logic, dynamic adaptability, and domain-specific intelligence to operate within complex, evolving environments.

Why AI agents matter

AI agents supercharge processes by integrating automation, artificial intelligence, and autonomous agents. They not only execute tasks but also analyze the context, adapt their behavior, and continuously optimize outcomes. This leads to processes that learn, adapt, and continuously improve, resulting in faster processes and better public services.

Language, data and context: the keys to automation

AI agents understand various types of "languages" – not just natural ones – such as APIs, code, database queries, and natural language. They can communicate with different systems, process data, and orchestrate complex workflows autonomously. This multilingual capability is the foundation of AI agents and allows them to enhance human capabilities, creating a "human-AI chemistry".

What is an AI agent?

An AI agent perceives its environment, processes data using algorithms and models (especially Large Language Models), and performs actions autonomously. They can understand and apply different "languages" and are capable of reasoning, contextual understanding, and creative problem-solving.

The anatomy of an AI agent

AI agents consist of several interlinked components working together:

Perception: Agents gather and analyze data from various sources.
Knowledge base: Agents store general knowledge, domain-specific knowledge, and specialized knowledge.
Decision-making: Agents make decisions using rule-based systems, machine learning, or neural networks.
Action: Agents perform actions autonomously, such as calling APIs, querying data, or interacting with users.
Learning: Agents learn from experience and adapt to changing conditions.
User information handling: Agents use metadata stores and feature stores to better understand context and generate personalized responses.

The evolution of AI: levels of autonomy in agentic AI

The autonomy of AI agents can be divided into levels of increasing maturity:

Level 0: no agent involvement
Level 1: AI-assisted
Level 2: AI-augmented
Level 3: AI-integrated
Level 4: independent operation
Level 5: fully autonomous

Each level represents increasing sophistication, starting with basic AI assistance, advancing through decision support and process integration, and culminating in independent, collaborative AI agents capable of self-improvement.

Multi-agent architectures

Multi-agent architectures integrate several specialized agents into a coordinated system. These systems consist of autonomous agents that take on different tasks, communicate with each other, and handle complex workflows. Multi-agent architectures can be designed in various ways, such as single agent, network architecture, supervisor architecture, hierarchical architecture, and custom architecture.

Collaboration in agent spaces across organizations

Imagine a shared agent environment in which agents operated by different public organizations can interact and coordinate tasks seamlessly across boundaries. This can automate routine processes, relieving both citizens and public employees from manual work.

On-premise vs. cloud

AI agents can be deployed on-premise or in the cloud. While on-premise environments offer full data sovereignty, they often face inherent limitations in hardware scalability, support for large-scale AI models, and maintaining high availability and connectivity. Sovereign cloud platforms offer a powerful alternative by combining cloud-native scalability with strict data sovereignty and compliance.

Platform overview: automation meets agent intelligence

The lines between automation platforms and agent platforms are increasingly blurring. Many providers now offer hybrid solutions that integrate intelligent agents into automation flows. This unlocks new possibilities but also demands deeper technical understanding, especially for complex or custom use cases.

Real-world uses cases in the public sector

AI agents can be used in various real-world use cases in the public sector, such as:

Automating social media posts with ChatGPT and Zapier
Handling citizen emails
Automating meter readings via WhatsApp
Automating services with Relevance AI

Monitoring and dashboards

Monitoring is a core element of any production-grade AI or workflow architecture. Tools like the Elastic Stack can be used for real-time dashboards and operational monitoring, providing transparency, identifying bottlenecks early, and enabling data-driven decisions.

Critical reflection and limitation

AI agents and automation platforms offer enormous efficiency gains but also bring challenges that must be consciously addressed, such as ethical and legal considerations, complexity and implementation effort, costs and resources, and risks and error sources.

Six steps public sector leaders should take now

Build a robust data foundation
Assess automation readiness at the system level
Select a suitable agentic runtime architecture, then design for context
Engineer prompts and interfaces systematically
Identify and prioritize use cases strategically
Monitor, test, and improve your system all the time

AI agents A detailed guide to game-changing multi-agent What’s inside Introduction01 Supercharging processes02 What is an AI agent? 14 Real-world uses cases in the public sector Automating social media posts with ChatGPT and Zapier15Handling citizen emails16 Monitoring and dashboards Critical reflection and limitation Six steps public sector leaders should take now What we have learnt from the rise of AI agents Introduction While there is a growing ecosystem of commercialplatforms – from open source to enterprise-grade –our goal here is to show what is possible and not todescribe single solutions. From citizen interactions The public sector is facing a significant skilledlabor shortage, which is worsening due to ongoingdemographic shifts. To address this, increasingautomation is essential, enabling public organizations AI agents sound futuristic – and they are. Think offully automated processes and intelligent assistantsthat solve problems on their own. That is exactlywhat AI agents bring to the table. So why should you In this point of view, we will shed light on whatmakes AI agents so powerful, how they work,and how you can smartly integrate them intoyour automation strategy. Whether it is for Automation without AI is like using a typewriter inthe age of computers; it is better than handwriting,but it misses out on the transformative potential and We will explore real-world use cases across sectors,including the public sector, where AI agents unlocknew potential in citizen communication andadministrative efficiency. With rising administrativeworkloads, limited staff resources, and growing Modern automation platforms provide a strongfoundation: you build workflows, transfer data,and automate repetitive tasks. But the realtransformation happens when you integrate AI Public sector environments differ from enterpriseuse: They require maximum data sovereignty,transparent decision logic, and integration intoexisting systems and responsibilities. Unlike privatecompanies, public administrations must also makesure every automated decision is legally accountable Recent researchby the Capgemini Research Institutesuggests that public sector organizations are awaketo this potential: 90% of those surveyed plan toimplement agentic AI in the next 2-3 years.1But thefield is evolving rapidly. The concept of agentic AIis still emerging and often complex. Many solutionsare experimental, fragmented, or deeply technical This point of view is designed to support publicsector technical leads in navigating thesecomplexities. It offers clarity, practical guidance, Why AI agentsmatter Supercharging processes What happens when you combine automation, artificial intelligence, and •Automation refers to systems that follow predefined rules to complete tasks •AI enhances automation by enabling systems to learn from data, recognizepatterns, and make data-driven decisions. •AI agents go a step further – they not only execute tasks but also analyze thecontext, adapt their behavior, and continuously optimize outcomes. When these three elements work together, processes do not just repeat; theyevolve. They learn, adapt, and continuously improve. That is the real supercharge: In the public sector, this is especially important for sustainability and performance.Generative AI (GenAI) – including foundation models and on-premise deployments– allows you to implement intelligent automation that meets the highest What are foundation models? Foundation models are large-scale AI systems trained on vast and diversedatasets, designed to be adaptable across a wide range of tasks. Their •Large Language Models(LLMs) for understanding and generatingtext for broad capabilities.•Small Language Models(SLMs) for lightweight VLMs (Vision Language Language, data and context: the models are not limited to natural language. They areequally capable of interpreting programming code, Language is not just English, German, or Chinese. Italso comes in the form of application programminginterfaces (APIs), machine languages, robotcommands, or control sequences. Yet it is not just When a model cannot only communicate inhuman language but also operates in machine At that point, it is no longer just about writing textor holding conversations. It is about controllingsystems, triggering processes, analyzing data, and Modern artificial intelligence can understand,process, and apply these different forms oflanguage in context. It extracts meaning from dataand adapts its actions based on what is relevant This multilingual capability is the foundation of AIagents. Because they understand various typesof languages – not just natural ones – they cancommunicate with different systems, process data, Large Language Models like Microsoft’s AzureOpenAI Service, OpenAI’s GPT-5, Google’s Gemini,Amazon’s Bedrock, and Mistral AI’s open-source What is an AI agent? An AI agent is an intelligent software system thatautonomously perceives its environmen

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