What Is an AI Agent?

An AI agent is a software system that can take action on behalf of a user to achieve a goal. Unlike traditional automation, an agent doesn’t just follow a fixed rule or script. It can:

• Understand intent using natural language

• Make decisions based on context and data

• Interact with tools, systems, or other agents

• Learn from outcomes through feedback loops

In simple terms:

An AI agent doesn’t just respond, it acts.

This is a major shift. Most AI-powered systems today still behave reactively. They wait for a prompt, generate an output, and stop. An agentic system goes further by deciding what to do next, often without constant human input.

That decision-making layer is what makes agentic AI both powerful and risky.

From Agents to Agentic Commerce

Agentic commerce is the application of AI agents to shopping, transactions, and customer relationships across a commerce platform.

Instead of a customer:

• Browsing products

• Comparing options

• Asking questions

• Completing purchases manually

An agentic shopping system can do this on their behalf.

Examples include:

• An AI agent that finds the best product based on preferences and budget

• An agent that monitors prices and completes purchases when conditions are met

• A customer service agent that resolves issues and triggers refunds or reorders

• Agents that handle agent payments, subscriptions, or restocking automatically

In other words, agentic commerce shifts the shopping experience from interaction-based to outcome-based.

The customer doesn’t need to click through a funnel anymore. They just need to express intent.

How Agentic Commerce Works (TL;DR)

If we strip agentic commerce down to its essentials, it follows a clear pattern. Understanding this flow is important, especially if you’re considering implementation.

The Core Loop of Agentic Commerce

At a high level, agentic commerce works through a continuous loop:

1. Intent detection: The AI agent interprets what the user wants using natural language, behavior, or historical context.

2. Context building: The agent pulls in relevant data: customer history, preferences, product data, availability, pricing, and constraints.

3. Decision-making: Using AI-driven reasoning, the agent evaluates options and chooses a course of action.

4. Action execution: This could include product discovery, completing purchases, triggering agent payments, or contacting customer service systems.

5. Feedback and learning: The outcome is evaluated. Did the user accept the result? Was the purchase returned? This feeds back into future decisions.

The feedback loop in step 5 is what separates agentic systems from simple AI tools, and also what creates the most efficient AI agents.

Agentic AI vs Generative AI: Why the Difference Matters

Generative AI has shaped how most people understand artificial intelligence in commerce. It writes product descriptions, answers customer questions, and improves product discovery by making information easier to access. In short, it’s very good at producing responses.

Agentic AI is fundamentally different. Instead of focusing on output, it focuses on outcomes. An agentic system doesn’t just explain options; it decides what to do next and takes action on behalf of the user. That could mean selecting a product, triggering agent payments, resolving a service issue, or completing purchases across a commerce platform.

This difference matters because generative AI stops once it delivers an answer. Agentic AI continues until a goal is achieved. That shift from responding to acting is what enables agentic commerce, but it’s also what introduces risk.

In practice, many so-called “shopping agents” today are still generative systems wrapped in conversational interfaces. They feel intelligent but rely heavily on user confirmation and predefined rules. True agentic AI requires reliable product data, clear permissions, and the ability to reason across multiple steps and moments in time.

The real differentiator is autonomy. Generative AI can be wrong in what it says. Agentic AI can be wrong in what it does. That’s why agentic commerce isn’t just about smarter artificial intelligence, but about careful design, governance, and trust.

A useful way to frame the distinction is simple:

Generative AI helps users understand.

Agentic AI helps users act.

Examples of Agentic AI Use Cases in the Real World

Agentic AI is already moving beyond theory and into practical commerce applications. Below are seven real-life use cases that show how agentic commerce works when systems are allowed to act, not just assist.

1. Autonomous product reordering

Platforms like Amazon use agent-like systems to predict when households need essentials and prompt or automate reorders. The agent monitors behavior, timing, and product data to reduce friction, not just recommend items.

2. AI-driven shopping assistants

Klarna has experimented with AI agents that guide users through product discovery, price comparisons, and merchant selection, acting as a personal shopping layer rather than a static search tool.

3. Customer service agents with execution rights

Some large retailers now deploy AI agents that don’t just answer questions but also issue refunds, replace items, or escalate issues automatically. This shortens resolution time and improves customer experience, while reducing support load.

4. Subscription and plan optimization

Telecom and SaaS companies use agentic systems to monitor usage and proactively suggest, or apply, plan changes. The agent acts in the customer’s interest, adjusting pricing or features without requiring manual requests.

5. Dynamic pricing and inventory agents

In travel and hospitality, AI agents continuously adjust prices based on demand, availability, and timing. These systems don’t just analyze data; they act by updating live offers across channels.

6. Agent-powered B2B procurement

Enterprise buyers increasingly use AI agents to manage recurring purchases, approvals, and supplier selection. Once constraints are set, the agent handles completing purchases and reconciliation automatically.

7. Post-purchase experience agents

After checkout, agents can track deliveries, handle returns, and suggest follow-up purchases, extending agentic commerce beyond the transaction into long-term customer relationships.

Progressing from Chatbots to Agents

Most companies begin with chatbots because they’re easy to deploy and relatively low risk. Chatbots answer questions, surface information, and reduce basic support load. But they remain reactive by design; they wait for input and hand decisions back to the user.

As customer expectations evolve, this model starts to feel inefficient. People don’t want instructions; they want outcomes. They don’t want to be told what to do next; they want the system to handle it.

Progressing from chatbots to agents is less about replacing interfaces and more about expanding responsibility. First, systems become better at understanding intent through natural language. Next, they retain context across interactions. The final step is granting them limited autonomy; the ability to take action across connected systems.

This transition requires trust, clear permissions, and reliable data. The most effective agentic commerce strategies evolve gradually, increasing autonomy only where it genuinely reduces friction and improves the customer experience.

Using Agentic Systems as Part of Customer Journeys & Commerce

Agentic systems create the most value when they are embedded across the full customer journey, rather than isolated in a single feature or channel. When designed well, they reduce effort, anticipate needs, and create more fluid customer experiences without removing human control.

Intent-Based Product Discovery

Traditional product discovery relies on navigation, filters, and search. Agentic systems shift this toward intent. Customers describe what they need in natural language, and the AI agent translates that intent into structured queries across product data. The result is fewer steps, less cognitive load, and faster relevance, especially in large or complex catalogs.

Guided Decision-Making, Not Endless Choice

Too much choice often leads to inaction. Agentic systems can evaluate trade-offs on behalf of the customer, weighing factors like price, availability, delivery speed, sustainability, or past preferences. Instead of presenting dozens of options, the agent makes a recommendation and explains its reasoning, helping customers feel confident rather than overwhelmed.

Assisted and Autonomous Purchasing

Agentic commerce doesn’t mean removing the customer from the loop entirely. In low-risk or recurring scenarios, agents can handle completing purchases automatically. In higher-risk cases, the agent prepares the transaction and asks for a single confirmation. This balance reduces friction while preserving trust and control.

Proactive Customer Service and Issue Resolution

Rather than waiting for complaints, agentic systems can monitor signals such as delivery delays, repeated failures, or abnormal usage patterns. When an issue is detected, the agent can trigger proactive actions - issuing refunds, sending replacements, or notifying support - which can often before the customer reaches out.

Post-Purchase Experience Management

The customer journey doesn’t end at checkout. Agentic systems can track orders, manage returns, and provide updates without requiring manual follow-up. They can also suggest relevant next steps, such as accessories, replenishments, or support content, based on real usage rather than assumptions.

Relationship and Loyalty Optimization

Over time, agentic systems build a deeper understanding of customer behavior. This enables more meaningful loyalty experiences by way of personalized rewards, relevant offers, and smarter timing. Instead of static segments, loyalty becomes dynamic and behavior-driven, strengthening long-term customer relationships.

Cross-Channel Continuity and Context

One of the biggest pain points in commerce is fragmented experiences across channels. Agentic systems can maintain context across chat, email, apps, and in-store interactions. Customers don’t need to repeat themselves, and journeys feel continuous rather than disconnected.

How to Implement Agentic Systems for Customer Experience

Implementing agentic systems is less about adding a new AI tool and more about making long-term commitments across data, infrastructure, and governance. Without these foundations, agentic commerce quickly becomes fragile or risky.

Data Readiness and Accessibility

Agentic systems depend on clean, reliable product data and customer data. Inventory, pricing, availability, and customer history must be accurate and accessible in real time. If data is outdated or fragmented across systems, agents will make poor decisions.

System Integration and Tooling

Agents need the ability to interact with real systems: commerce platforms, payment providers, customer service tools, and analytics layers. This requires APIs, permissions, and clear boundaries around what an agent can execute autonomously versus what requires human approval.

Clear Use Case Scoping

The safest way to introduce agentic commerce is by starting narrow. Focus on specific use cases such as product discovery, post-purchase support, or subscription management. Each use case should have defined goals, success metrics, and failure states before autonomy is increased.

Governance, Permissions, and Human Oversight

Agentic systems must operate within strict rules. This includes approval thresholds, audit logs, and escalation paths to humans. Autonomy should be earned gradually, not assumed from day one.

How Important Are Feedback Loops for AI Agents?

Feedback loops are essential for agentic AI. Unlike static automation, agents learn from outcomes. Did the customer accept the recommendation? Was the purchase returned? Did the intervention reduce support tickets?

Without feedback, agents repeat mistakes at scale. With well-designed loops, they improve decision-making over time, becoming more accurate, more personalized, and more aligned with business goals. Feedback is what turns autonomy into long-term value rather than risk.

What Are the Risks of Agentic Commerce?

The biggest risk in agentic commerce is misplaced trust. When agents act on behalf of users, mistakes have real consequences, whether they be financial, operational, or reputational. Poor data, unclear permissions, or over-automation can damage customer relationships quickly.

There’s also the risk of over-removing humans from the loop. Not every decision should be automated, especially in high-stakes or emotionally sensitive scenarios. The most successful agentic commerce strategies balance autonomy with transparency and control, ensuring AI acts as a trusted partner rather than an invisible authority.

The Role of Trust in Agentic Commerce

Trust is the foundation of agentic commerce. When AI agents are allowed to act on behalf of customers, whether recommending products, handling customer service, or completing purchases, users must believe the system is working in their best interest.

This trust is built through transparency and control. Customers need to understand what an agent can do, why it made a decision, and when human intervention is possible. Silent autonomy erodes confidence; explainable actions strengthen it.

Reliability matters just as much. An agent that occasionally fails is manageable. An agent that fails unpredictably is not. Consistent performance, clear boundaries, and visible safeguards are what turn agentic systems from experimental features into trusted parts of the shopping experience.

Ultimately, the future of agentic commerce won’t be decided by how advanced artificial intelligence becomes, but by how confidently customers are willing to delegate decisions to it.

Conclusion

Agentic commerce marks a shift from AI that informs to AI that acts. When implemented responsibly, agentic systems reduce friction, improve customer experience, and create more meaningful customer relationships. But autonomy comes with risk, requiring strong data foundations, clear governance, and trust by design. The future of agentic commerce won’t be defined by how intelligent AI becomes, but by how well businesses balance action, control, and accountability across the entire customer journey.