The world of AI is moving at lightning speed. Since ChatGPT burst onto the scene in 2022, we've seen an explosion of new tools and technologies. Similar problems are being reimagined with a fundamentally different technology powering the solutions. For those of us in the enterprise world, it can be tough to cut through the noise and figure out which solutions will actually make a difference. This article breaks down three key concepts: Traditional Workflows, Generative AI, and the new frontier, Agentic AI. Understanding the difference is crucial for applying the right technology to the right problem and driving real business impact.
1. Traditional Workflows: The Reliable Rule-Follower 🔄
Traditional workflows are the backbone of modern business automation. They are rule-based systems designed to execute a predefined sequence of tasks with precision and consistency. Think of them as a train on a track—it follows a set path from A to B every single time.
-
How it Works: These workflows are powered by "if-then" logic. If a specific trigger occurs (e.g., an employee submits a form), then a predetermined action is taken (e.g., the form is sent to a manager for approval). They are fantastic for high-volume, repetitive tasks that don't require judgment or creativity.
- Human Involvement: A human designs the workflow upfront, setting all the rules, steps, and conditions. Once launched, the system runs on its own, but it can't deviate from the path it was given.
Prior to the onset of Large Language Models, machine learning models were being used to dynamically adapt workflows based on patterns and feedback loops. Learn more about Predictive Intelligence offering on the platform.
2. Generative AI: The Creative Content-Spinner ✍️
Generative AI, powered by large language models (LLMs), is a major leap forward. Instead of just following rules, it understands and creates new, original content. Think of it less like a train and more like a talented writer who can draft an article on any topic you give them.
-
How it Works: You give it a prompt (an instruction or a question), and the AI generates a response—whether that's text, images, or code. It recognizes patterns in data to produce statistically likely and contextually relevant outputs.
-
Human Involvement: Humans are in the driver's seat, providing the initial prompt and refining the output. It's a powerful tool for augmenting human creativity and productivity, but it doesn't take action on its own.
Learn more about generative AI offerings in Now Assist for HR Service Delivery.
3. Agentic AI: The Autonomous Problem-Solver 🚀
Agentic AI is the next evolution. It combines the content-creation power of Generative AI with the ability to take autonomous, multi-step actions to achieve a goal. Think of an Agentic AI not as a writer, but as a project manager. You give it a high-level objective, and it figures out the steps, uses different tools, and works independently to get the job done.
-
How it Works: An AI Agent can reason, plan, and execute a series of tasks. It can interact with different applications, access information, and make decisions to overcome obstacles. If one approach fails, it can try another.
-
Human Involvement: A human sets the final goal, but the AI handles the "how." The user delegates the entire process, not just a single task.
Here are some key characteristics of Agentic AI:
i.) Goal Driven Action: It operates with a distinct goal in mind, breaking down complex objectives into manageable steps and acting autonomously to achieve them.
ii.) Reasoning & Planning: It can assess various options, anticipate outcomes, and make context-aware decisions often in collaboration with other agents.
iii.) Tool Utilization: It can access & process information from the internet, databases, APIs, and engages with software interfaces.
iv.) Continuous Learning & Adaptation: Through interactions and feedback, it refines its understanding, improves decision-making, and increases effectiveness over time.
Refer to the guidance below for understanding the suitability of technology to drive the intended business outcomes
Caution: AI Agents may not always be the best solution to solve a problem. Consider qualifying potential use cases based on whether reasoning, orchestration, and collaboration are required—some use cases may be better suited by other methods like Now Assist skills invoked manually by the UI (single prompt actions).
AI Agents for HR
You can learn more about AI Agent capabilities released for HR in May 2025. Our subsequent release provides AI Agents for the following areas to accelerate business outcomes for HR functions:
Generating Onboarding Ramp-up Plans
Tailored ramp up plans are critical for onboarding employees but are an overhead for busy managers.
This team of agents in this use case will save managers time by creating tailor made plans using a variety of data inputs, which will in turn improve ESAT and time to productivity.
This team of agents will augment the existing deterministic onboarding workflow by:
- Personalized learning content tasks
- Team-specific tasks, 1:1 intros invites tasks
- Organize tasks in appropriate stages
- Interfacing with the manager for any edits and add to current journey
Resolving HR Cases
Despite trying to provide self-service options, employees often submit general inquiries through portal or email channels. These requests are costly because they require human triage & intervention.
Cases needing human agent needs them to spend considerable time understanding the context, reviewing organizational policies, employee's HR profile , FAQs, service fulfillment instructions, and business playbooks to formulate a resolution plan.
These AI Agents:
- Automate the resolution of routine employee inquiries which speeds up MTTR and reduces cost for HR operations organizations
- Provide sensitivity detection to ensure that the employee is connected to a live agent for topics that require human empathy, such as sexual harassment
- Executes a contextual resolution plan for Tier 1 cases, accelerating resolution by human agents
Creating Job Requisitions
The job requisition process is slow, costly, and hard to scale, with hiring managers often unsure where to start or what’s required. This leads to delays, high costs, and lower candidate quality. This team of agents will reduce time to open and cost per hire by gathering inputs, understanding policies, and refining requisition details, which will lead to faster, more accurate hires and improved candidate quality.
This team of agents is responsible for:
- Gathering input from an initial request
- Understanding intent & internal requisition policies and procedures
- Refining required requisition details
- Drafting and refining job descriptions
- Creating and assigning requisitions
Interview Scheduling
Interview scheduling is complex and time-consuming, often leading to coordination headaches and delays. Recruiters and coordinators spend valuable time juggling calendars and managing communications, which can slow hiring and impact experience. This team of agents will streamline scheduling by interpreting intent, recommending interviewers, finding optimal time slots, and handling communications — which will improve time to schedule and enhance candidate satisfaction.
This team of agents is responsible for:
- Interpreting user intent and requirements for interview structure
- Formulates an interview plan by recommending interviewers & evaluation mechanism
- Identifying optimal interview slots for selected attendees and resolving calendar conflicts
- Drafting personalized communication for attendees
- Sending invites to attendees with relevant information & interview insights
- Learning from user preferences & process context to optimize scheduling
Key reads to learn more about Agentic AI offering on the platform:
- AI Agents Implementation Guides in Now Create
- Introducing AI Agents and Quick Start Guide
- AI Agent tools – Getting the most out of your agentic workflows
- Create your own AI Agent! A walkthrough on creating an AI Agent using AI Agent Studio
- AI Agents FAQ and Troubleshooting
- Introducing AI Agent Fabric - Enable MCP and A2A for your agentic workflows
- Introducing Model Provider Flexibility: Optimize Every AI Workflow
- Protecting Sensitive Data in Generative AI: How to Use Data Privacy for Now Assist
If you have questions or thoughts, feel free to drop them in the comments—we’ll respond or update the article as needed. If you found this article helpful, please share your feedback or link to it on your preferred platform.
For tailored guidance, reach out to your ServiceNow account team.
