Generative AI and agentic AI are two complementary approaches to using artificial intelligence. Generative AI (GenAI) creates content in response to prompts; agentic AI plans and executes multistep tasks in pursuit of a goal you set.
Both agentic AI and GenAI are effective tools for ecommerce, but only if you provide oversight. For example, you might use a GenAI tool to write product descriptions, or have an agentic system automatically issue refunds to customers whose return requests meet preset criteria. Both types of AI systems require the right guardrails.
Here’s a quick guide to the key differences between agentic AI and gen AI and when to use each.
What is agentic AI and how does it work?
Agentic AI is an artificial intelligence system that can pursue goals across multiple steps with minimal human input.
Give an agentic AI system an objective (like “find the fastest shipping option at the lowest cost”) and it can identify the steps needed to achieve that goal, research every option, call upon external tools or data sources as needed, check the results of each action, and present the final answer for approval. If you’ve given it permission to do so, the agent might even select that shipping method and initiate fulfillment.
For example, Shopify Sidekick is an AI agent that can handle automated workflow management, customize app functionality, identify insights from store data, and more. You might ask Sidekick to tag customers who’ve spent more than a certain dollar amount as VIPs and set up a special discount code. The tool interprets the request, checks customer order histories, applies the tag to qualifying customers, and generates the discount code. After you’ve reviewed and approved the process, you could tell Shopify Flow to automatically send the code to future VIP customers.
Customers can also use AI agents to assist with shopping, and Agentic Storefronts make your products discoverable to popular AI platforms like ChatGPT, Microsoft Copilot, and Google AI Mode. This means a shopper can task an agent with finding relevant products, then choose what they want to buy and complete the purchase, often without ever leaving the chat interface.
What is generative AI and how does it work?
Generative AI excels at creating content—text, images, audio, video, or software code—based on natural language instructions. Give a GenAI model a simple text prompt, and it can interpret your words, produce the content you request, and then wait for further input.
Most GenAI tools rely on sophisticated large language models (LLMs) trained on massive datasets drawn from books, websites, media, and computer programs. LLMs learn the statistical patterns in language and use them to predict the most likely next piece of output, one step at a time until a complete result takes shape.
GenAI tools are especially adept at generating code. Adoption among developers is now near-universal. JetBrains’ 2025 State of Developer Ecosystem survey found that 85% of more than 24,000 coders regularly use AI tools for software development.
To see GenAI in action, you might use Shopify Sidekick for its GenAI capabilities—for instance, to create a product catalog. Enter text describing the product features and keywords you’d like to include, and Sidekick can generate a polished product description you can revise or publish. You can also use it to draft blog posts, compose answers to customer queries, generate custom theme code, and modify images—all using simple text prompts.
Agentic AI vs. generative AI: Core differences
The simplest way to describe the difference between GenAI and agentic AI is that one is reactive while the other is proactive. Gen AI models react to your commands. You can tell them to write a letter, draw a picture, or compose music, and they’ll produce it, then wait for your next instruction. Agentic AI acts on its own to carry out steps toward a goal, rather than waiting for each command.
Think of generative AI as a talented chef able to whip up any dish you order. Agentic AI can manage the entire restaurant, running the kitchen, serving the food, handling the books, and hiring staff. It combines the creative capability of GenAI with the ability to research, plan, and carry out a sophisticated sequence of tasks. But because AI agents operate with minimal human oversight, they also require stronger safeguards around the tasks they’re allowed to automate.
Here are key differentiators between the two:
| Dimension | Generative AI | Agentic AI |
| Core function |
Creation Produces text, images, audio, video, or code in response to a prompt. |
Execution Plans and performs a sequence of actions involving multiple steps. |
| Autonomy |
Low Requires a human to review, revise, and act on the output. |
High Operates with minimal human input; may only bring up a decision point for approval. |
| Interaction with other systems |
Occasional Can pull in outside information to improve responses. |
Frequent Actively draws on external data sources and tools and takes actions based on what it learns. |
| Memory and continuity |
Basic Can retain context within or across conversations to inform its next response. |
Advanced Maintains context across steps, checks its own results, and adjusts course as it goes. |
| Major risk factor |
Accuracy Gen AI models produce confident-sounding output that can be factually wrong. Human fact-checking is essential. |
Autonomy Agentic AI can take actions that may be difficult to reverse (like incorrectly issuing refunds). AI agents require clear guardrails limiting what they’re allowed to do without human oversight. |
Ways to use agentic AI and GenAI together
- Promotional discount campaigns
- Inventory restocking
- Abandoned cart recovery
- Customer review requests
- Seasonal collection launches
- Customer win-back campaigns
- Support ticket triage
Gen AI and agentic systems like Shopify Sidekick are powerful tools, and they provide real value when they combine to automate repetitive tasks. Here are some potential joint use cases for agentic AI and GenAI:
Promotional discount campaigns
A GenAI tool can draft copy for a promotional campaign that offers customers different discounts based on the dollar value of products they’ve purchased. An agentic AI system then checks sales records, determines what discount each customer qualifies for, prepares the appropriate emails, and schedules them for delivery after your final approval.
A second agentic workflow analyzes the campaign’s success, tallies how many customers took advantage of the offers, has the GenAI tool draft reminder emails to those who haven’t, and schedules those to go out a few days later.
Inventory restocking
When a product’s stock drops below a set threshold, an agentic AI tool can flag it and check historical sales velocity to estimate how much to reorder.
It then prompts a GenAI tool to draft the reorder request to the supplier, tailored to that vendor’s usual format and terms. You review the draft, and the agentic tool schedules and sends it after you’ve approved it.
Abandoned cart recovery
An agentic AI tool monitors checkout activity and identifies customers who abandoned their carts after the total reached a specified dollar value. It then prompts GenAI to draft a personalized recovery email for each segment, referencing the specific items left behind and offering an incentive to complete the purchase.
The agentic tool then schedules the send sequence and tracks which customers come back to check out, helping you determine which customer segments the sequence resonated with the most.
Customer review requests
An agentic AI tool detects when a product has been successfully delivered, then uses a GenAI tool to draft an email referencing the item by name and asking for feedback in a tone consistent with your brand voice.
The agentic tool schedules the message and follows up once with anyone who hasn’t responded within the time frame you set.
Seasonal collection launches
As you prepare to launch a new seasonal line, GenAI drafts product descriptions and marketing copy for each item based on the keywords and photos you provide.
An agentic AI tool then checks competitor sites to help you land on the right price point, publishes the listings you approve, and sets up the accompanying promotional emails and social posts around the product launch date.
Customer win-back campaigns
An agentic AI tool segments customers who haven’t placed an order in 90 days or more, grouping them by the items they’ve previously purchased.
GenAI drafts a winback offer for each segment, personalizing each message by referencing the customer’s past purchases. The agentic tool schedules the campaign and reports back on how many lapsed customers returned.
Support ticket triage
When a customer asks about their order, an agentic AI customer service tool checks the order and shipping status itself, then has GenAI draft a reply in your brand’s usual tone.
For straightforward cases, like news that a package is in transit, the agentic tool might send that reply directly. Anything more complicated—a lost package, a damaged item, an angry customer—gets escalated to a human rep along with a summary of what the agent found.
Agentic AI vs. generative AI FAQ
When should I use generative AI in my business?
Gen AI is best suited to content creation tasks where you’re able to review the output and catch any factual errors before you publish or send it. That might mean product descriptions, marketing copy, blog posts, images, and emails.
When should I use agentic AI in my business?
Turn to agentic AI for repetitive, multi-step tasks you’d rather not babysit at each stage, like checking inventory and reordering, segmenting customers and running a campaign, or looking up an order and resolving a support ticket. It pays off when the task recurs often enough to justify the setup, and when you’re willing to define upfront how much it can do without your approval.
Can gen AI and agentic AI work together?
Gen AI and agentic AI can, and often do, work together. Agentic AI frequently uses a generative AI model as one step inside a larger sequence it’s managing. A common pattern is an agentic tool handling the research, decision-making, and scheduling around a task, while calling on generative AI to write customer-facing copy when you need to produce content.
