The Next Leap in Enterprise AI: ServiceNow Unveils AI Agent Fabric

ServiceNow’s Knowledge 2025 event in Las Vegas was abuzz with AI advancements, and a standout announcement was the introduction of AI Agent Fabric. (Launched alongside the AI Control Tower, AI Agent Fabric is engineered to serve as a sophisticated communication backbone for entire AI ecosystems, facilitating native collaboration between diverse AI agents, tools, and systems.. This is a significant step towards creating a truly interconnected and intelligent enterprise, where AI from various providers can work in concert.

For ServiceNow developers and product owners, AI Agent Fabric isn’t just another feature; it’s a foundational shift that opens up a new realm of possibilities for building complex, end-to-end intelligent automation.

What Exactly is AI Agent Fabric?

At its core, AI Agent Fabric is designed to solve the growing challenge of AI interoperability. As organizations deploy more AI agents – both from ServiceNow and third-party vendors – the need for these agents to communicate, share context, and coordinate actions becomes paramount.

AI Agent Fabric acts as this crucial “dynamic connected layer,” enabling different AI systems to:

• Communicate Agent-to-Agent (A2A): Allowing direct interaction and task delegation between different AI agents.
• Interact Agent-to-Tool: Enabling AI agents to utilize external tools and services.
• Connect System-to-System: Facilitating communication between broader agentic systems.

This allows ServiceNow’s native AI agents to work seamlessly alongside third-party agents (from partners like Adobe, Box, Cisco, Google Cloud, IBM, Microsoft, UKG, Zoom, and more) and even custom-built agents developed via ServiceNow’s AI Agent Studio. The goal is to enable these disparate AI components to dynamically exchange information, coordinate on tasks, and collectively take action in real-time.

The Powerhouse Protocols: MCP and A2A

Two key enablers for AI Agent Fabric are common interoperability protocols: Model Context Protocol (MCP) and Agent2Agent (A2A) protocol.

• Model Context Protocol (MCP): Originally introduced by Anthropic and now seeing wider adoption (including by Microsoft and Google), MCP provides a standardized way for AI models to access external data, tools, and services. Think of it as a universal adapter that allows an AI agent to understand what “tools” another system or agent offers and how to use them. It helps in cleanly passing context and enabling an AI model to request actions from an external source. For example, an MCP server might expose capabilities like “fetch customer details,” “analyze document in Box,” or “summarize webpage.”

• Agent2Agent (A2A) protocol: As the name suggests, A2A focuses on enabling direct communication and collaboration between different AI agents. Google Cloud, for instance, has been involved with A2A protocols. This allows one AI agent to delegate tasks to another, share findings, and work together on more complex problems that may require specialized skills from different agents. ServiceNow and UKG, for example, are leveraging A2A for cross-platform AI agent integration in HR and workforce management.

By supporting these protocols, AI Agent Fabric ensures that ServiceNow can become a central hub for orchestrating a diverse ecosystem of intelligent agents, regardless of their origin.

A Universe of Use Cases: What AI Agent Fabric Unlocks

The ability for different AI agents to communicate and collaborate seamlessly opens up a vast array of powerful use cases across the ServiceNow platform. Here are some potential scenarios:

1. Hyper-Automated Incident & Threat Resolution (ITSM & SecOps):

• A ServiceNow ITOM AI agent detects a critical P1 infrastructure alert.
• Using A2A, it communicates with a specialized third-party cybersecurity AI agent (e.g., from a partner like Cisco or Microsoft) to perform an in-depth threat analysis on the affected configuration items (CIs).
• The security AI, using MCP to understand the context (CI details, network logs provided by the ServiceNow agent), returns its findings.
• The ServiceNow ITSM agent then creates a high-priority security incident, enriching it with the security AI’s analysis. It could further use A2A to instruct a network automation AI agent to isolate the affected segment or a SecOps AI agent to initiate a remediation playbook.

2. Proactive and Contextual Customer Service (CSM):

• A third-party sentiment analysis AI (e.g., integrated via a partner) detects a high-value customer expressing significant frustration on a public forum.
• It alerts a ServiceNow CSM AI agent via A2A.
• The CSM AI agent uses MCP to gather comprehensive customer context: recent support tickets, purchase history from an integrated ERP, and interaction details from the CRM.
• Armed with this 360-degree view, the CSM AI can:
  • Draft a personalized, empathetic outreach message for a human agent to review and send.
  • Proactively create a case, assigning it to a specialized team.
  • Use A2A to consult a product knowledge AI agent for specific troubleshooting steps if the issue is technical.

3. Streamlined Employee Lifecycle Management (HRSD & ITAM):

• When a new employee is hired, a ServiceNow HRSD AI agent kicks off the onboarding workflow.
• Via A2A, it instructs an ITAM AI agent to provision necessary hardware and software licenses.
• The ITAM agent, using MCP to get employee details (role, department) from the HRSD agent, places orders and sets up accounts.
• Simultaneously, the HRSD agent could use A2A to communicate with a third-party learning management system’s AI agent to assign initial training modules, and a facilities AI agent to arrange workspace and physical access.

4. Intelligent Supply Chain & Procurement Orchestration (SPM & Custom Apps):

• An external AI agent monitoring global supply chain risks identifies a potential disruption affecting a critical component.
• It notifies a ServiceNow-based procurement AI agent using A2A, sharing details of the disruption (component ID, estimated impact, alternative sources if known) via MCP.
• The ServiceNow procurement agent then:
  • Checks internal inventory levels.
  • Uses A2A to query an integrated supplier risk assessment AI (perhaps from a financial services partner) for the viability of alternative suppliers.
  • Initiates automated quote requests or purchase orders through ServiceNow workflows.

5. Collaborative Content Intelligence & Workflow (Cross-Departmental with Partners like Box):

• A sales representative working in ServiceNow CRM needs to generate a complex proposal.
• A ServiceNow sales AI agent, through AI Agent Fabric and A2A, invokes a Box AI agent.
• The Box AI agent, using its specialized content intelligence and MCP to understand the requirements (e.g., “find all case studies relevant to X industry and Y product”), retrieves and summarizes relevant documents securely stored in Box.
• The findings are passed back to the ServiceNow agent, which then helps assemble the proposal or provides insights to the sales rep. Similar use cases exist for HR onboarding (extracting details from new hire paperwork in Box) or customer service (summarizing manuals from Box).

6. Integrated Development and Operations (DevOps & ITOM):

• During a CI/CD pipeline run, an AI agent integrated with the development tools (e.g., a Jit or Microsoft AI agent) detects a new code vulnerability.
• It uses A2A to alert a ServiceNow ITOM/SecOps AI agent, passing vulnerability details and affected code context using MCP.
• The ServiceNow AI agent can then automatically create a vulnerability response task, link it to the relevant application CI, assess business impact based on CSDM data, and assign it to the appropriate development team, potentially even suggesting remediation steps.

Benefits for All

The introduction of AI Agent Fabric brings tangible benefits for everyone in the ServiceNow ecosystem:

Benefits for End Users (Employees & Customers):

• Faster Issue Resolution: Seamless collaboration between AI agents means quicker diagnosis and resolution of problems, whether it’s an IT ticket, an HR query, or a customer service request.
• More Proactive Service: AI agents can anticipate needs and address potential issues before they impact users, such as proactively notifying a customer about a service disruption and the steps being taken.
• Personalized and Contextual Experiences: By sharing data and context, different AI agents can contribute to a more holistic understanding of the user, leading to more tailored and relevant interactions.
• Simplified Self-Service: Users can get answers and complete tasks more easily through intelligent self-service portals powered by collaborating AI agents that can handle multi-step, cross-system requests.
• Reduced Friction: Smoother handoffs between different systems and departments mean users don’t have to repeat information or navigate complex processes.

Benefits for Fulfillers (IT Staff, HR Agents, Customer Service Agents, etc.):

• Reduced Manual and Repetitive Work: AI Agent Fabric allows for the automation of more complex, multi-system tasks, freeing up human fulfillers from routine activities.
• Access to Richer, Contextual Information: When AI agents collaborate, they can consolidate information from various sources, providing fulfillers with a comprehensive view to make better decisions.
• Focus on High-Value, Complex Tasks: By automating the automatable, fulfillers can concentrate their expertise on issues that genuinely require human ingenuity and empathy.
• Faster Task Completion and Increased Productivity: With AI agents handling initial data gathering, analysis, and even preliminary actions, fulfillers can complete their assigned tasks more efficiently.
• Enhanced Decision Support: AI agents can provide summaries, recommendations, and insights, empowering fulfillers to make more informed decisions quickly.
• Streamlined Cross-Departmental Collaboration: AI Agent Fabric can facilitate smoother workflows that span multiple departments, reducing delays and improving coordination for fulfillers involved in these processes.

For Product Owners:

• Solve Complex Business Problems: Tackle challenges that require the coordinated intelligence of multiple AI systems spanning different departments or even different enterprises.
• Drive End-to-End Automation: Achieve deeper and broader automation by enabling AI agents to manage complex processes that cross system boundaries.
• Accelerate Innovation: Rapidly prototype and deploy innovative solutions by leveraging a fabric that supports dynamic collaboration among AI agents.
• Enhanced ROI from AI: Maximize the return on AI investments by enabling AI assets to work together more effectively, managed and governed by the AI Control Tower.

For Developers:

• Simplified Integration: More easily connect and orchestrate diverse AI capabilities without deep custom integration for each pair of systems.
• Focus on Value-Added Logic: Spend less time on the “plumbing” of AI communication and more on designing sophisticated, high-impact intelligent workflows.
• Build Composite AI Solutions: Combine the strengths of ServiceNow’s platform AI with best-of-breed specialized AI agents from third parties to create more powerful and nuanced applications.
• Future-Proofing: Build solutions on a framework designed for an expanding ecosystem of AI agents and tools.

The Dawn of the Interconnected AI Ecosystem

AI Agent Fabric, in conjunction with the AI Control Tower and Workflow Data Fabric, solidifies ServiceNow’s strategy to be the central nervous system for enterprise AI. By fostering an open and collaborative environment where AI agents can freely communicate and coordinate, ServiceNow is paving the way for a new era of intelligent automation that delivers superior experiences for everyone.

For ServiceNow developers and product owners, this is an invitation to think bigger – to envision and build solutions where diverse AI capabilities converge to drive unprecedented levels of efficiency, insight, user satisfaction, and business value. The fabric is laid; it’s time to weave the future of intelligent workflows.