ServiceNow AI Agents: What They Are, How Do They Work, What Do They Do?

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ServiceNow AI Agents go beyond “AI chat” — they can understand a goal and execute workflow steps inside the Now Platform, like updating records, routing approvals, and triggering automations (with guardrails). In this article, we explain what they are, how they work, and the most practical use cases to start delivering measurable ROI.

ServiceNow AI Agents are “digital coworkers” inside the Now Platform that can understand a goal, decide the next steps, and take actions in workflows (create/update records, route approvals, draft responses, run checks, trigger automations) - with guardrails and (depending on setup) human approval when needed.

Think of the difference like this:

Copilot / assistant: helps you do the work (suggests text, summarizes, drafts).

AI agent: can do parts of the work itself across steps (triage → gather info → update ticket → notify people → propose solution), and then hand back a result for review.

Key milestones:

June 13, 2023 – ServiceNow introduced Now Assist (genAI assistance) starting with Virtual Agent experiences.

September 10, 2024 – ServiceNow publicly announced AI Agents for areas like IT, Customer Service, HR, Procurement, etc., plus the Now Assist Skill Kit (to build custom skills/abilities agents can use). Limited availability was expected starting November 2024 for early use cases.

January 29, 2025 – ServiceNow announced broader agentic AI capabilities like AI Agent Orchestrator and AI Agent Studio (building + coordinating agents), with availability planned around March 2025.

2026 Zurich release cycle – ServiceNow documentation shows ongoing enhancements to Now Assist AI Agents in the Zurich release.

What’s the goal?

ServiceNow’s goal is basically:

1) Move from “chatting” to “getting work done”

Instead of AI just answering questions, agents complete tasks end-to-end inside the same workflow system where your company already runs work.

2) Scale productivity 24/7 without chaos

They’re aiming for “always-on” support where agents handle repetitive steps, freeing humans for exceptions and judgment calls—but still controlled through roles, approvals, and governance.

3) Keep it governed and auditable

Because agents can take actions, ServiceNow emphasizes access control, permissions, and safe execution modes (who can trigger agents, what they can do, when they need supervision).

A simple example

Employee: “My laptop VPN keeps failing.” AI agent: checks the incident history, runs a guided diagnostic, gathers logs, suggests a fix, updates the incident record, and if it can’t solve it, routes to the right team with a clean summary and evidence.

How Do AI Agents Work?

The technology at the heart of the AI agent is the large language model (LLM). A powerful class of machine learning (ML) systems designed to process and generate natural language, LLMs are the engine behind an AI agent’s ability to understand goals, break them into tasks, and communicate their solutions effectively. However, LLMs alone are not enough for AI agents to fully execute complex, multi-step tasks. This is where ‘tool calling’ comes into play. AI agents can extend their capabilities by using external tools, such as APIs, databases, or even other AI models, to gather real-time information, analyze data, and adapt their workflows.

AI agents continuously evolve through feedback loops and iterative refinement—learning from their actions and adjusting based on outcomes and human input, where needed. This adaptability allows AI agents to improve decision-making and optimize performance over time. To do this, these agents follow a specific sequence of stages:

Goal definition and task planning The process begins with the user providing the AI agent with a specific goal or objective. Once the goal is set, the AI agent initiates planning by breaking down the objective into smaller, actionable tasks. For more complex goals, the AI agent maps out an entire sequence of subtasks, creating a complete roadmap to help direct its actions in future stages.

Data gathering and knowledge acquisition To carry out the tasks and subtasks identified in the previous stage, AI agents need access to relevant information. They gather data from various sources (internet, internal databases, external tools, etc.). In cases where the AI agent lacks specific knowledge, it can use APIs or connect with other systems to help fill in the gaps.

Decision-making and execution Once equipped with the necessary data, the AI agent employs machine learning models to make decisions. It evaluates the information, determines a possible course of action, and begins executing the tasks.

Monitoring and feedback integration As the AI agent progresses through its tasks, it continuously monitors the results of its actions, gathering feedback from both its environment and the user. This feedback is essential for self-assessment and governance, as it allows the AI agent to adjust its approach if needed. The AI agent can also create new subtasks based on the feedback it receives, ensuring that it stays aligned with the user's ultimate goal.

Learning and improvement After completing a task, the AI agent stores the data and lessons learned in its knowledge base. This allows it to refine its strategies for future interactions. Over time, this process makes it possible for the AI agent to become more accurate and efficient.

Over the next 12–24 months, the winners won’t be the companies with the most AI pilots, but the ones that can deploy trusted agents at scale: governed, auditable, connected to real workflows, and measured on outcomes. The big shift is simple: AI won’t just answer questions — it will execute work. And that means your operating model changes too: new ways to design processes, manage risk, and prove ROI.