Intelligence meets innovation: new features and tools
ServiceNow is opening a new chapter with the Zurich release. The platform has undergone continuous development since the first release, Aspen, in 2012. Now, with the letter Z, it has not only reached the end of the alphabetical cycle, but also a milestone in terms of agentic AI, security, and workflow innovation.
Agentic AI: From assistant to teammate
While AI was previously intended purely as a support tool, Zurich is bringing about a decisive change: AI agents work independently and contextually. They become part of everyday work, not just tools.
- Multi-agentic AI: Not just a single agent, but multiple agents can work together, coordinate complex tasks, and take on coordinated workflows.
- Build Agent: Turn ideas into production-ready apps with voice commands—with debugging, test generation, logic, and integration, without deep technical knowledge.
- Developer Sandbox: Isolated environments where teams can work in parallel, with versioning and testing possible before anything goes live. This reduces risks and speeds up feedback loops.
Security as a foundation
With increasing automation, the attack surface also grows. The Zurich version addresses this challenge with an integrated security architecture:
- The ServiceNow Vault Console combines encryption, data protection, zero trust access, and code signing, and offers dashboards for monitoring and recommendations for protecting newly discovered sensitive data.
- The Machine Identity Console controls API integrations and bots, identifies outdated or insecure authentication methods, and highlights vulnerabilities in machine identities.
- The AI Control Tower provides platform teams with centralized visibility, governance, and compliance across all AI agents, models, and workflows. This enables the transition from experimentation to reliable production use.
Agentic Playbooks and Process and Task Mining Insights
The process side is also getting an upgrade:
- Agentic Playbooks combine human input and AI in workflows (“hybrid control”). Tasks are performed automatically, and humans can intervene if necessary. Examples: credit card support, identity verification, replacement requests.
- Process and task mining insights are now fully embedded in the platform. They help identify how work actually flows, where bottlenecks exist, and where agents have the greatest impact.
Now Assist and GenAI features
The Zurich release also expands ServiceNow’s GenAI capabilities:
- Support for multiple LLMs (e.g., Azure OpenAI, Gemini, Claude) for greater flexibility.
- Improved translation and email response capabilities with automatic citations.
- Enhanced panel capabilities in Now Assist for greater clarity and a better user experience.
Advanced developer tools
There are additional new features for developers:
- Workflow Data Fabric Hub: Real-time access to external data sources without copying.
- App Engine Management Center: Centralized control and governance of low-code apps.
- Performance Analyzer in Studio: Detailed analysis of load times and optimization options.
Impact and Operations
There are also innovations in the area of operations and monitoring:
- ServiceNow Impact offers AI-powered root cause analysis in Instance Observer.
- Automatic summaries of KPIs in natural language facilitate decision-making.
ServiceNow Zurich Release: The Future of the Platform
With the Zurich Release, ServiceNow combines intelligence, accelerated development, and security on a unified platform. Agentic AI is taking the next step: moving away from pure support to becoming an active co-creator. For companies, this means:
- Greater innovative strength
- Faster time-to-value
- Higher security, even with complex, AI-supported workflows
- Better governance and control, even at larger scales
The Zurich update impressively demonstrates how human creativity and machine intelligence can work together, setting a new standard for the future of work.
Frequently asked questions:
What are AI agents?
AI agents are AI-powered software components that can do more than analyze or answer tasks; they can execute them in a goal-oriented way. They understand requests, evaluate information, make rule-based or AI-supported decisions, and then trigger specific actions in processes, workflows, or systems. Unlike traditional chatbots, AI agents do not simply respond to individual questions. They can plan multiple steps, consider context, use data from different sources, and independently handle tasks within defined guardrails.
What is the difference between GenAI and AI agents?
Generative AI creates content such as text, summaries, answers, code, or recommendations. AI agents go one step further: They use generative AI, decision logic, data, and integrations to actively execute tasks. Example: GenAI can summarize a support ticket. An AI agent can also check which category fits, analyze similar cases, prepare solution recommendations, start a workflow, or route the ticket to the right team.
What is the difference between a chatbot and an AI agent?
A chatbot typically answers questions or conducts simple dialogs. An AI agent can also plan tasks, evaluate information, support decisions, and trigger actions in connected systems or workflows. While a chatbot is often focused on communication, an AI agent combines communication, contextual understanding, automation, and process execution.
What benefits do AI agents offer companies?
AI agents can reduce repetitive tasks, shorten processing times, and relieve employees in their day-to-day work. They help teams find information faster, execute processes more consistently, and handle service requests more efficiently. AI agents are especially valuable wherever many similar tasks, large volumes of data, or complex process chains come together. Examples include IT Service Management, Customer Service, HR, Finance, Field Service, Operations, and Enterprise Service Management.
Where can AI agents be used?
AI agents are suitable for many areas of the enterprise where requests, decisions, data, and workflows are interconnected. Typical use cases include IT service, customer service, HR processes, knowledge management, process automation, incident management, request fulfillment, change management, AIOps, and case management. For example, they can classify tickets, suggest knowledge articles, process service requests, prepare approvals, analyze incidents, or guide employees through complex processes.
Do AI agents replace employees?
AI agents are primarily intended to support and relieve employees. They take over repetitive, time-consuming, or highly standardized tasks so that specialists have more time for complex decisions, customer interaction, and value-adding activities. In many scenarios, AI agents therefore do not work fully autonomously, but rather act as assistance systems with clear responsibilities, escalation rules, and human oversight.
How do companies get started with AI agents?
The best way to get started is with clearly defined use cases that deliver measurable value while remaining easy to control. Suitable examples include frequent support requests, ticket classification, knowledge management, summaries, simple approval processes, or standardized service requests. It is important not to focus on technology alone, but to consider processes, data, roles, responsibilities, and governance from the very beginning.
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