How Should I Start Learning LLMs & Agentic AI in ServiceNow? (Need Guidance)

[Community Alums]

Hi everyone 

I’m a recent graduate currently building my skills in the ServiceNow ecosystem. While learning the platform, I became really interested in how AI is evolving—especially with LLMs and Agentic AI.

The idea that AI can not only assist but also understand context, make decisions, and take actions inside workflows is something I want to deeply explore.

However, I’m honestly a bit confused about where to start and how to structure my learning path 

      I’d love your suggestions on:

• How to start with LLMs in the context of ServiceNow

• What fundamentals I should learn first (AI basics, APIs, prompt engineering?)

• How Agentic AI is actually implemented in ServiceNow (like Now Assist, Virtual Agent, etc.)

• Which tools/modules to focus on for hands-on learning

 My goal is to learn ServiceNow Agentic AI + AI/LLMs to build smart, automated solutions.

If you’ve worked in this space or are learning it, your guidance would really help me move in the right direction 

Thanks in advance!

#ServiceNow #AgenticAI #LLM #AI #NowPlatform #FreshGraduate #LearningJourney #Automation #Developers

[glideFather]

ahoy @Community Alums,

The Now Assist beginner guide nobody asked for but everyone needed is available: [GF#8] Getting started with Now Assist

[yashkamde]

Hello @Community Alums ,

I'd suggest First of all understand the difference between Gen AI & Agentic AI which confuses the most in base understanding,

Refer below for the practical difference,

Generative AI	Agentic AI
Generates content such as text, summaries, code, or knowledge articles based on user prompts.	Plans, decides, and executes tasks autonomously to achieve a defined goal.
Responds to prompts but does not independently decide the next action.	Evaluates context and determines the next best action without constant user input.
Assists users by providing suggestions or generated content.	Performs end-to-end workflow automation by coordinating multiple steps.
Typically requires user review or approval before actions are taken.	Can execute actions such as updating records, triggering flows, creating tasks, and calling APIs based on configured permissions.
Best suited for content generation, summarization, translation, and code generation.	Best suited for incident resolution, request fulfillment, employee onboarding, and process orchestration.
Example: Now Assist summarizes an incident or generates a knowledge article.	Example: An AI Agent investigates an incident, gathers information, creates tasks, updates records, and notifies stakeholders automatically.

Then I’d suggest starting with ServiceNow fundamentals, JavaScript, REST APIs, and prompt engineering before going deep into Agentic AI. Then explore Now Assist, AI Agents, Flow Designer, and IntegrationHub through real-world use cases rather than just theory.

Build small Use cases where AI understands context, makes a decision, and triggers an automated workflow this is the best way to understand Agentic AI practically.

Most importantly, practice as many real-time use cases as possible, hands-on experience will make the concepts much clearer.
Also you can practice the below use cases for Agentic AI:

1) Incident Auto-Triage & Routing Agent : Classify incoming incidents by category,priority, auto-assign to the assignment group.

Approach : Use AI Agent with a "Classification" skill + reference qualifier scripts on assignment_group based on predicted category.

2) Duplicate Incident Detector : Flag incidents that are likely duplicates of an existing major incident.

Approach : Use Now Assist Search on short_description + description, set similarity, auto-link as "related".

3) Resolution Notes Summarizer Agent : Auto generate closure notes from work notes or activity log.

Approach : Trigger agent on state change to "Resolved", pass work_notes journal field into a summarization skill, write to close_notes.

4) Change Risk Assessment Agent : Predict risk of a change request based on historical change failures. Approach : Feed change fields (CI, type, past related incidents etc) into agent, output risk score to a custom field, flag if high risk.

If my response helped mark as helpful and accept the solution.

[masonreed11]

Start with ServiceNow fundamentals like Flow Designer, scripting, REST APIs, and then move into LLM basics and prompt engineering. Once comfortable, explore Now Assist and Virtual Agent through small projects. Hands-on practice with simple AI-powered workflows will help you understand Agentic AI much faster.

[Shahjay]

Hello @Community Alums ,

SInce you're just starting out, I'd flip the usual order and treat this as platform work first, model theory second

Now Assist is the generative layer — summaries, draft content, catalog help. AI Agents are the agentic piece: they plan steps, call tools, update records, and run flows, all within whatever permissions you give them. If Flow Designer, GlideRecord, ACLs, and REST still feel shaky, agents will be a struggle before anything else clicks.

A path that actually matches how the product's built: start with ServiceNow University's Introduction to Generative AI, then Now Assist Essentials, then AI Agents Essentials, then move into the Now Assist AI Agents Deep Dive path. Those courses assume you already know Flow Designer, Virtual Agent, and a bit of scripting.

Once you've got that, spin up a PDI, install whatever Now Assist pieces you're licensed for, and open AI Agent Studio. Build one tiny agent that does one thing — summarizing an incident's work notes into close notes when the state hits Resolved is a good first pick. That alone teaches you skills, tools, instructions, and channel availability (Now Assist panel vs. Virtual Agent) without needing to design anything huge.

From there, give the agent a tool that's a Flow or a Script Include, so it can actually assign a group or spin up a child task. That's the real jump — from a chat that writes text to an agent that does work. Keep the instructions tight. Vague prompts are the number one reason agents wander off track.

After that, spend time on prompt structure inside the product itself — role, allowed tools, what it shouldn't do, and when it should stop and ask instead of guessing. Learn how Now Assist uses your instance data too, and what you should never put in a prompt. IntegrationHub and REST become relevant right around here, since almost every useful agent ends up calling something external.

For your first few projects, stick to the basics: auto-triage an incident off the short description, flag a likely duplicate, draft resolution notes, or kick off a catalog item from Virtual Agent. Get comfortable with those before you even think about a multi-agent setup.

You don't need to train an LLM for any of this. You need to be solid on ServiceNow data, Flow, and writing tight instructions — Now Assist and AI Agent Studio do the rest on top of that.