Agentic AI vs. AI Agents: Tips for Designing Agentic Systems

The terms “agentic AI” and “AI agents” are often used interchangeably, and understandably so—they’re closely related. But they’re not exactly the same thing.

Agentic AI refers to a configuration in which individual AI agents operate and sometimes collaborate. You might build an AI agent to automate a simple task, like a generative AI tool that responds to customer questions using natural language processing. In an agentic AI environment, that customer service bot is simply one of multiple AI agents that interact with each other. 

Read on to learn how agentic AI systems complete tasks and how to use them to automate complex workflows.

What is an AI agent?

An AI agent is an artificial intelligence system powered by a large language model (LLM)—the same AI technology driving gen AI tools like ChatGPT and Claude—that’s able to take actions autonomously. You tell the AI agent what you want it to do, and it figures out how to achieve that goal by understanding your instructions, reasoning through problems, and deciding what to do next.

Unlike a traditional generative AI chatbot that responds to questions one at a time, an AI agent can handle a number of tasks in sequence, use external tools, and adapt based on what it learns along the way. You can use platforms like Shopify’s agent builder to define the agent’s goals, the tools it can access, and the boundaries it operates within.

“An agent is kind of like a purpose-specific configuration of AI,” says Alex Pilon, Shopify developer and AI advocate. “You have a system (or instruction) prompt that is tuned for a particular task or workflow, and then the agent can be configured with different types of tools—such as access to databases and third-party web services—and resources, such as a product knowledge base or training materials from your own business.”

What is agentic AI?

Agentic AI refers to the environment in which multiple AI agents operate and collaborate. Rather than a single agent handling everything, agentic AI systems typically involve multiple agents working in sequence, each handling a specific part of a larger workflow with minimal human input.

In an agentic commerce context, that might look like this: A customer submits a return request. The first agent receives it and looks up the order; a second agent checks the store’s return policy and approves or flags the request; a third routes the approved return into a refund queue; a fourth updates the customer’s order history; a fifth sends a follow-up email with a satisfaction survey. No human intervention is required unless something falls outside the rules and gets escalated for review.

In this scenario, each agent is asked to do one thing. Working together, AI agents complete tasks you’d otherwise need to handle manually using multiple tools.

The connective tissue of an agentic system is an orchestrator—typically an LLM configured to receive input, assess it, and decide what happens next. A typical orchestrator in an agentic system follows the same basic loop: receive, classify, route, escalate.

For example, the orchestrator might receive all inbound customer requests and triage them: a return goes to the returns agent, a shipping question goes to the fulfillment agent, a billing dispute gets flagged for human review.

How to set up an agentic AI system

1. Map the end-to-end workflow
2. Determine what triggers each agent
3. Define what each agent does with its output
4. Set boundaries
5. Decide what happens when an agent can’t complete its task
6. Assign responsibility for the workflow after launch

Creating an agentic AI system means assembling multiple agents into a coordinated workflow, where each handles a specific task, passes results to the next, and escalates to a human when something falls outside its rules. Building that orchestration layer requires defining the workflow explicitly before any agent is deployed:

1. Map the end-to-end workflow

Before you decide how many agents you need and what each one does, map the full process from trigger to conclusion. When handling a return request, for example, every step from initial customer contact to refund confirmation needs to be accounted for.

2. Determine what triggers each agent

Define the specific input or event that puts each agent to work. For example, the orchestrator receives a classified return request and hands it to the returns agent. The returns agent approves the refund and hands it to the refund processing agent.

Clearly define each trigger. If triggers are vague or overlapping, agents will either sit idle or step on each other.

3. Define what each agent does with its output

Define what each agent produces and where that output goes. Does it pass a structured result to the next agent? Update a database record? Trigger a customer-facing communication? An agent that completes its task but has nowhere to send the result breaks the chain just as effectively as one that fails outright.

4. Set boundaries

Specify exactly what each agent is and isn’t authorized to do. An agent handling return approvals shouldn’t have the ability to issue refunds directly; that task should be assigned to a separate agent with its own authorization logic—or require human oversight. Tight boundaries limit the damage when something goes wrong.

5. Decide what happens when an agent can’t complete its task

Every agent needs a defined failure path. What counts as a failure? When a failure occurs, is the task returned to the orchestrator, sent to a different agent, or escalated to a human? 

For example, if a valued customer attempts to return a product outside your store’s 90-day return policy, does the agent simply deny the request automatically or pass it to a human for special consideration? Clear escalation paths are a core element of well-defined agentic AI systems. 

6. Assign responsibility for the workflow after launch

Agentic systems will encounter edge cases no one anticipated during design. Someone needs to be in charge of monitoring performance, reviewing escalations, and updating agent configurations as the workflow evolves. Decide before launch who that person is and what human oversight looks like in practice.

Tips for rolling out agentic AI systems

• Prioritize clean data pipelines
• Choose the right agentic AI platform
• Retain human oversight

Here are a few prerequisites you’ll need in place before you can build a functional agentic AI system: 

Prioritize clean data pipelines

Agents need reliable access to your order management system, inventory database, customer relationship management (CRM) system, email software, or any other system they’re meant to act on. This data needs to be current and consistent. Giving an agent stale or siloed data may cause it to make confident decisions based on the wrong information.

Choose the right agentic AI platform

Options for creating agents range from no-code agent builders like Shopify’s to frameworks like LangChain and AutoGen, which offer more control over the agent’s logic, tools, and memory but may require hiring one or more developers.

Retain human oversight

You’ll need predefined rules that lay out specific tasks agents are authorized to perform and what each agent is forbidden to do. You’ll also need to define the conditions under which a task leaves the agentic workflow and requires human input. Continuous monitoring keeps tabs on output when agentic AI systems encounter edge cases no one anticipated.

Agentic AI pitfalls to avoid

• Scope creep at the agent level
• Cascading errors
• Over-automation
• Lack of observability

Deploying AI agents to handle core ecommerce services requires careful planning. Here are potential pitfalls to be aware of:

Scope creep at the agent level

Agents given broad tool access and loosely defined goals may take actions their designers didn’t intend.

For example, a customer service agent with access to a store’s inventory system may see a backlog of orders for out-of-stock items and begin canceling them—even when customers are willing to wait. The agent needs access to inventory systems to answer customer questions, but it should be constrained from taking actions beyond its original scope.

Cascading errors

In a sequential pipeline, a mistake by the first agent becomes the input for the second. Say a customer submits a return request using the wrong order number. If the first agent fails to verify the order information, it could direct other agents to approve the return and issue a refund to the wrong customer. 

Avoid this by building in validation checkpoints—verification steps that confirm the previous output against real data—before the next agent takes action, especially those that are hard to reverse, such as issuing refunds, updating account information, or sending customer-facing communications. 

Over-automation

Keeping humans in the loop at high-stakes decision points can help make agentic AI systems more trustworthy.

For example, a fraud detection agent may cancel an order because a customer’s shipping address no longer matches their billing address. A person reviewing the transaction may recognize the customer is sending a gift and allow the order to go through. 

Lack of observability

If you don’t record what your AI agents did and why, you can’t fix what goes wrong. 

Say a merchant notices that some customers are complaining about receiving order confirmations listing the wrong items. Without logs, there’s no way to tell which agent generated the confirmations, what inputs it was working from, or how many customers were affected and never complained. 

Any agentic platform you use should automatically log the actions of its agents. If you’re deploying a custom agentic system, you want to make sure it records the events that triggered each agent, what the agent did, and what the outcome was.