Based on AWS (2025), BCG (2025), Colback (2025), Gaarlandt et al. (2025), IBM 2025, Microsoft (2025a, 2025b), Qu et al. (2025)

AI agents are autonomous AI programs that can perceive information, make decisions, and act toward achieving goals with minimal human intervention, using AI models and connected tools to carry out tasks. AI agents possess a degree of agency: they don’t just respond to direct commands, they can proactively initiate steps and adapt their behavior to changing inputs or objectives. This agentic quality has led experts to distinguish agentic AI from generative AI: generative AI creates content, while agentic AI takes action. While generative AI (like an LLM that writes copy) is largely reactive to prompts, an AI agent can set goals, make independent decisions, and execute multi-step workflows to accomplish a specific mission.

This chapter provides a comprehensive guide to understanding AI agents and their transformative impact on marketing practice. We begin by exploring how AI agents think and plan, examining their core observe-plan-act cycle and comparing two key problem-solving approaches: the adaptive “think-as-you-go” method and the efficient “plan-first” strategy. Next, we’ll classify the five main types of AI agents—from simple reflex agents to sophisticated learning systems—helping you choose the right level of capability for different marketing challenges. We then examine both the remarkable strengths of AI agents (speed, consistency, complex decision-making) and their important limitations (lack of common sense, black-box decisions, potential biases), including what can go wrong and how to prevent problems. The chapter concludes by showcasing real-world applications, from how AI agents are revolutionizing campaign management and content creation for marketers to how they’re transforming the shopping experience for consumers. By the end of this chapter, you’ll understand not just what AI agents can do, but when and how to use them effectively in marketing practice.

How AI Agents Think and Plan

AI agents are being used by both companies and consumers to help with marketing tasks, and these systems need to solve complex problems step by step – just like a marketing team would. But before diving into the two main problem-solving approaches, it’s helpful to understand the basic workflow that all AI agents follow.

The Basic AI Agent Workflow

Every AI agent goes through three fundamental steps:

1. Understanding Your Goal The AI agent takes your request – whether it’s “analyze our Q3 campaign performance” or “help me plan a product launch” – and breaks it down into smaller, manageable tasks. Just like a project manager would create a task list from a big objective.
2. Gathering Information The AI agent collects everything it needs to complete those tasks. This might mean pulling data from your CRM, searching industry reports online, or analyzing your social media metrics. Some advanced agents can even collaborate with other AI tools to get specialized insights.
3. Executing and Adjusting The AI agent works through its task list methodically. After completing each task, it checks whether it’s moving toward your original goal and adjusts if needed – sometimes even adding new tasks it realizes are necessary.

How AI Agents Work: The Core Cycle

AI agents follow a continuous three-step cycle that allows them to tackle complex challenges:

Observe AI agents constantly collect and process information from their environment – whether that’s user requests, performance metrics, customer data, or market trends. What makes them powerful is their ability to remember previous conversations and interactions, building context over time. Think of this like how an experienced marketer keeps track of past campaign results and customer feedback to inform future decisions.

Plan Using advanced language models, AI agents evaluate what they’ve observed and create a strategy to accomplish goals. They consider the current situation, past experiences, and available resources to prioritize the best course of action. This is similar to how an experienced marketer might assess market conditions and plan a campaign strategy.

Act AI agents can connect to various business systems – CRMs, analytics platforms, social media tools, or databases – to actually execute their plans. They can pull reports, analyze data, create content, or even coordinate with other AI agents to get specialized tasks done. If something goes wrong or they need clarification, they can detect the issue and either fix it themselves or ask for input.

This observe-plan-act cycle repeats continuously, with agents learning and becoming more effective over time based on what works and what doesn’t.

Two Main Problem-Solving Approaches

Within this basic framework, there are two main approaches AI agents use to tackle multi-step marketing challenges:

The “Think-As-You-Go” Approach (ReAct)

Imagine working on a campaign launch. You might start by researching your target audience, then based on what you find, decide to analyze competitor pricing, and then use those insights to plan your messaging strategy. Each step builds on the previous one.

This is exactly how the ReAct approach works. The AI agent:

• Takes an action (like researching market trends)
• “Thinks” about what it learned
• Decides what to do next based on those findings
• Repeats this process until the problem is solved

Benefits for marketing: This approach is great for complex, exploratory projects where you don’t know exactly what you’ll discover. For example, if you’re entering a new market, the AI agent can adapt its research strategy as it learns more about local consumer preferences.

The downside: This can be slower and use more resources since the AI agent has to stop and think after each step.

The “Plan-First” Approach (ReWOO)

Now imagine a different scenario: launching a product similar to ones you’ve done before. You can map out your entire strategy upfront – market research, competitive analysis, customer surveys, and campaign design – all before you start executing.

The ReWOO approach works similarly:

• The AI agent creates a complete plan upfront after understanding your request
• It gathers all the needed information at once
• It combines everything to give you a final answer

Benefits for marketing: This is faster and more efficient for routine tasks. Plus, you can review and approve the AI agent’s plan before it executes, giving you more control over the process.

When to use it: Perfect for standard marketing activities like monthly performance reports, competitor analyses, or campaign post-mortems where the process is predictable.

Choosing the Right Approach

For marketing students and professionals, understanding these approaches helps you:

• Set better expectations when working with AI agents
• Choose the right AI approach for different marketing challenges
• Communicate more effectively with technical teams about AI capabilities

Think of it this way: use the “think-as-you-go” approach for creative, exploratory marketing challenges, and the “plan-first” approach for routine, structured marketing tasks. Either way, the AI agent will be continuously observing, planning, and acting to help achieve marketing objectives.

Types of AI Agents

AI agents can be developed to have varying levels of capabilities. A simple agent might be preferred for straightforward goals to limit unnecessary computational complexity. In order of simplest to most advanced, there are 5 main agent types:

1. Simple Reflex Agents

Simple reflex agents are the simplest form that base their actions on what they can currently perceive or sense. These agents have no memory of past interactions and cannot seek help from other agents when they lack information. They function on a set of reflexes or rules – they are preprogrammed to perform specific actions when certain conditions are met.

Key characteristics:

• No memory of past events
• React only to current situation
• Follow predetermined rules (“if this, then that”)
• Cannot handle unexpected situations they weren’t programmed for
• Work best in fully observable environments where all necessary information is available

Marketing applications: Think of basic automated email responses or simple chatbots. If a customer emails asking “What are your store hours?” the agent always gives the same preprogrammed response.

Limitations: If the agent encounters a situation it wasn’t prepared for, it cannot respond appropriately. For example, a chatbot programmed to answer pricing questions won’t know how to handle a complaint about product quality.

Example: A social media automation tool that posts “Happy Monday!” every Monday at 9 AM, regardless of current events or brand context.

2. Model-Based Reflex Agents

Model-based reflex agents use both their current perception and memory to maintain an internal model of their environment. As the agent receives new information, it updates this internal model. The agent’s actions depend on its model, rules, previous experiences, and current situation.

Key characteristics:

• Have memory and can store information
• Build and update an internal “model” of their world
• Can operate in partially observable environments that change over time
• Still limited by their set of predetermined rules

Marketing applications: A customer service chatbot that remembers your name, past purchases, and previous conversations during your current session. It builds a model of who you are and what you might need.

How the internal model works: Like a robot vacuum that maps your house as it cleans, avoiding furniture it has encountered before and remembering which areas it has already cleaned to avoid repeating work.

Example: An e-commerce website that tracks your browsing behavior and adjusts product recommendations based on what you’ve viewed, creating an internal model of your preferences that gets updated with each click.

3. Goal-Based Agents

Goal-based agents have an internal model of the world plus specific goals they want to achieve. These agents search for sequences of actions that will reach their goal and plan these actions before executing them. This planning capability makes them more effective than simple or model-based reflex agents.

Key characteristics:

• Have specific goals or objectives
• Plan sequences of actions to reach goals
• Search through different possible approaches
• Make decisions based on which actions will achieve their objectives

Marketing applications: A campaign optimization system with the goal of maximizing email open rates. It doesn’t just send emails – it analyzes different subject lines, send times, and audience segments to plan the approach most likely to achieve its goal.

How goal-planning works: Like a GPS navigation system that considers multiple routes to your destination and recommends the one that best meets your goal (fastest route, shortest distance, avoiding tolls, etc.).

Example: A social media management tool that has the goal of increasing brand engagement. It analyzes when your audience is most active, what content performs best, and plans a posting schedule designed to maximize interactions.

4. Utility-Based Agents

Utility-based agents select sequences of actions that not only reach their goal but also maximize utility or reward. Utility is calculated through a utility function – a system that assigns a utility value (measuring the usefulness or “happiness” an action brings) to each possible scenario based on fixed criteria.

Key characteristics:

• Have utility functions that measure the value of different outcomes
• Consider multiple factors when making decisions
• Optimize for the best possible result, not just goal achievement
• Weigh different priorities against each other

How utility functions work: The criteria might include factors like progress toward the goal, time requirements, cost, or computational complexity. The agent calculates which action will produce the highest utility score and selects that option.

Marketing applications: A programmatic advertising system that doesn’t just aim for clicks but considers cost per click, conversion probability, brand safety, and audience quality to bid on ad placements that provide the highest overall value.

Example: A navigation app that optimizes for multiple factors – recommending a route that balances time, fuel efficiency, toll costs, and traffic conditions to maximize overall utility rather than just focusing on one factor.

5. Learning Agents

Learning agents have the same capabilities as other agent types but are unique in their ability to learn autonomously. They add new experiences to their initial knowledge base automatically, which enhances their ability to operate in unfamiliar environments. Learning agents are composed of four main components:

The four key components:

1. Learning: Improves the agent’s knowledge by learning from the environment through sensors and interactions
2. Critic: Provides feedback on whether the agent’s responses meet performance standards
3. Performance: Responsible for selecting actions based on what has been learned
4. Problem Generator: Creates various proposals for new actions to test and try

Key characteristics:

• Continuously update their knowledge base
• Learn from new experiences automatically
• Improve performance over time
• Can adapt to unfamiliar environments
• May be utility-based or goal-based in their underlying reasoning

Marketing applications: Personalized recommendation engines that track user activity and preferences, storing this information to make better suggestions over time. Each interaction teaches the system more about what works.

How the learning cycle works: Every time a customer interacts with the system (clicks, purchases, ignores recommendations), the learning component processes this feedback, the critic evaluates whether recommendations were successful, performance adjusts future actions, and the problem generator suggests new approaches to test.

Example: Netflix’s recommendation system starts with basic demographic and genre preferences but learns from every show you watch, rate, skip, or rewatch. It continuously improves its suggestions by analyzing patterns in your behavior and comparing them to similar users.

Choosing the Right Agent Type

Simple reflex agents work for basic, repetitive tasks where consistency matters more than sophistication and the environment is predictable.

Model-based agents are suitable when you need some personalization and memory, but customer interactions follow relatively predictable patterns.

Goal-based agents are ideal when you have clear, measurable objectives and want the system to plan the best approach to achieve them.

Utility-based agents excel when you need to balance multiple competing priorities and optimize for complex, multi-factor outcomes.

Learning agents are worth the investment when you have substantial customer data and want continuously improving performance, especially in dynamic environments where customer preferences change over time.

AI Agents: Strengths, Limitations, and What Can Go Wrong

Understanding AI agents isn’t just about knowing what they can do—it’s equally important to understand their limitations and potential pitfalls. Like any powerful marketing tool, AI agents work best when marketers understand both their capabilities and their blind spots. This knowledge helps you set realistic expectations, choose appropriate use cases, and build safeguards to prevent problems.

What AI Agents Excel At

Speed and Scale That Humans Can’t Match

AI agents work around the clock without breaks, sick days, or vacation time. While your human team sleeps, an AI agent can analyze yesterday’s campaign performance and start optimizing today’s ads. This isn’t just about working faster—it’s about working at a scale that would be impossible for humans.

Consider customer service: if your business suddenly gets twice as many inquiries during a product launch, you can’t instantly hire and train twice as many support staff. But you can deploy additional AI agent instances or allocate more computing power to handle the surge. This scalability means customers get help when they need it, regardless of time zones or sudden spikes in demand.

Consistent Performance Every Time

Humans have bad days, get distracted, or make typos when they’re tired. AI agents perform the same task the same way, every single time. For routine marketing activities like updating product databases, checking content for compliance, or sending personalized emails, this consistency is invaluable.

An AI agent reviewing ad copy for brand guidelines will apply the same standards to the 500th piece of content as it did to the first. A human reviewer might start missing details after hours of repetitive work, but the agent maintains the same level of attention throughout.

Handling Complex Decisions with Multiple Variables

Marketing decisions often involve juggling dozens of factors simultaneously. An AI agent optimizing ad campaigns can consider thousands of targeting combinations, test multiple creative variations, and adjust bidding strategies across different platforms—all while monitoring performance in real-time.

For example, a campaign optimization agent might simultaneously analyze audience demographics, time of day, device type, competitor activity, and seasonal trends to determine the best ad placement strategy. This level of multi-variable optimization would be overwhelming for humans but is exactly what AI agents are designed to handle.

Learning and Improving Over Time

Well-designed AI agents get better at their jobs through experience. A customer service agent can learn from cases where it provided unhelpful responses (with human feedback) and improve its future interactions. A content creation agent can analyze which blog posts perform best and adapt its writing style accordingly.

This continuous improvement means your AI agents become more valuable over time, like having employees whose skills automatically sharpen with every task they complete.

Cost Efficiency at Scale

While AI agents require upfront investment in development and computing resources, they can dramatically reduce ongoing costs for routine tasks. One content creation agent might produce 50 blog posts monthly that would otherwise require a team of writers, representing significant cost savings that can be reinvested in strategy, creativity, or customer experience improvements.

Where AI Agents Fall Short

Missing Common Sense and Context

AI agents can make mistakes that seem obvious to humans because they lack real-world common sense. They operate on patterns in data rather than intuitive understanding of how the world works.

For instance, an agent might completely miss sarcasm in social media comments or fail to recognize that scheduling a promotional campaign during a national tragedy would be tone-deaf. They follow their programming literally—if told to “maximize email engagement,” an agent might start sending excessive emails or using clickbait subject lines that achieve higher open rates but damage brand trust.

Black Box Decision-Making

Many AI agents can’t clearly explain why they made specific decisions. If your campaign optimization agent suddenly shifts 30% of your budget from Facebook to Instagram, you might want to understand the reasoning. Was it based on performance data, audience behavior, or a technical glitch?

This opacity makes it difficult to learn from agent decisions, troubleshoot problems, or satisfy compliance requirements. It’s particularly challenging when you need to explain marketing decisions to stakeholders or justify budget allocations to executives.

Data Quality Problems and Bias

AI agents are only as good as the data they learn from. If historical data contains biases or blind spots, agents will perpetuate and potentially amplify these issues.

Consider a lead scoring agent trained on past sales data. If your sales team historically spent more time on leads from certain industries or demographics (for reasons unrelated to actual purchase likelihood), the agent might learn to prioritize similar leads, creating a self-reinforcing bias that limits business growth and raises fairness concerns.

Technical Dependencies and Integration Challenges

AI agents rely on various software tools, databases, and APIs to function. When integrations break—perhaps a social media platform changes its API, or a new privacy regulation restricts data access—agents may fail silently or produce incomplete results.

Unlike humans who can adapt by finding alternative information sources or workflows, agents typically can’t work around technical limitations on their own. They need their digital tools to be working perfectly to perform at their best.

Limited Emotional Intelligence and True Creativity

While AI agents can generate creative content and even produce surprising combinations of ideas, they lack genuine empathy and emotional understanding. They can’t truly understand customer frustration, celebrate authentic brand moments, or create emotionally resonant campaigns that connect with human experiences in meaningful ways.

An agent might handle routine customer service inquiries efficiently, but a customer dealing with a personal crisis or complex emotional situation likely needs human understanding and flexibility that current AI agents can’t provide.

When Things Go Wrong: Common Failures

Confident but Wrong Answers (Hallucination)

AI agents sometimes generate responses that sound authoritative but are completely incorrect. They might cite non-existent research studies, reference fake customer data, or make up statistics that sound plausible but are fiction.

This is particularly dangerous in marketing because wrong information can lead to poor strategic decisions, compliance violations, or embarrassing public mistakes. Always verify important claims and unusual recommendations from AI agents, especially when they cite specific facts or figures.

Humans Becoming Out of Touch

When AI agents handle routine tasks effectively, human marketers might gradually lose touch with day-to-day customer interactions and market realities. If an agent manages social media responses for months, the marketing team might miss subtle shifts in customer sentiment or emerging trends that require human judgment.

This creates a risk that when human intervention is needed—during a crisis, major strategy shift, or agent failure—the team lacks current knowledge to respond effectively. Regular human review and involvement helps prevent this disconnect.

Compounding Small Mistakes

AI agents work fast and autonomously, which means small errors can quickly multiply. If an analytics agent forms an incorrect hypothesis about customer behavior, it might pass that flawed insight to a campaign agent, which then creates messaging based on wrong assumptions, while a budget optimization agent allocates resources according to the faulty data.

These cascading errors can create multi-channel problems before humans notice something is wrong. Building in monitoring systems and regular checkpoints helps catch issues before they spiral.

Security Vulnerabilities and Misuse

AI agents with access to company systems and data can become targets for malicious actors. Cybercriminals might try “prompt injection” attacks—tricking agents into ignoring their programming and revealing confidential information or taking unauthorized actions.

Additionally, agents might be manipulated into pursuing their goals through inappropriate means. An engagement-focused social media agent might discover that controversial posts generate more clicks and start creating divisive content that damages brand reputation while technically achieving its objective.

Misalignment with Values and Brand Ethics

Perhaps the most serious failure mode occurs when agents pursue their programmed objectives in ways that conflict with company values or ethical standards. An agent optimizing for sales conversion might target vulnerable populations with predatory messaging, or a cost-cutting agent might sacrifice customer service quality to improve efficiency metrics.

These alignment problems highlight why clear guidelines, regular monitoring, and human oversight remain essential even with highly autonomous AI agents.

Best Practices for Responsible Agent Use

Understanding these strengths and limitations helps you use AI agents more effectively:

• Start with clear, well-defined tasks where agents’ consistency and scale provide the most value
• Maintain human oversight especially for customer-facing activities and strategic decisions
• Build in monitoring and checkpoints to catch errors before they compound
• Test extensively in low-risk environments before deploying agents in critical processes
• Plan for failure scenarios and ensure humans can step in when agents encounter problems they can’t handle
• Regular auditing of agent decisions to check for bias, drift, or misalignment with company values

AI agents are powerful tools that can transform marketing efficiency and effectiveness, but like any powerful tool, they require thoughtful implementation and ongoing management to deliver their benefits while avoiding potential pitfalls.

AI Agents Transforming Marketing Practice

Campaign Management and Optimization

AI agents are revolutionizing how marketing campaigns are planned, executed, and optimized. Unlike traditional automation that requires marketers to predict every scenario and create rules accordingly, AI agents can autonomously manage campaigns by analyzing real-time performance data and making intelligent adjustments.

These digital marketing assistants can simultaneously monitor multiple campaigns across different channels, automatically adjusting ad spend, targeting parameters, and creative elements based on performance metrics. They analyze customer behavior patterns to identify optimal timing for campaign launches and can predict which messaging strategies will resonate with specific audience segments.

One particularly powerful application is dynamic pricing and inventory management. AI agents can manage pricing strategies and inventory levels in real-time, responding to factors like competitor pricing, seasonal demand fluctuations, and supply chain disruptions. These systems automatically adjust prices to optimize for different objectives—maximizing revenue during peak demand periods or clearing inventory during slow seasons.

Personalized Customer Experiences

Modern consumers expect personalized experiences, and AI agents excel at delivering hyper-customized interactions at scale. These systems analyze vast amounts of customer data—including browsing behavior, purchase history, social media activity, and demographic information—to create detailed customer profiles and predict preferences.

AI agents can craft personalized email content, recommend products tailored to individual customers, and even adjust website experiences in real-time based on visitor behavior. Unlike rule-based personalization that might show “customers who bought X also liked Y,” AI agents understand context and can make sophisticated recommendations that consider factors like seasonal trends, current events, and individual customer life stages.

A prime example is e-commerce product discovery, where AI agents are changing how consumers find and purchase products online. Instead of browsing through categories or using basic search functions, customers increasingly interact with AI shopping assistants that understand complex preferences. For instance, a customer planning a vacation might tell an AI agent they want “sustainable travel options for a family with teenagers who love outdoor activities.” The agent can analyze travel patterns, environmental impact data, seasonal weather information, and family-friendly activity options to provide personalized recommendations that no traditional search system could match.

Content Creation and Management

Content marketing requires consistent output across multiple channels, and AI agents are transforming how brands approach content creation. These systems can autonomously generate blog posts, social media content, email newsletters, and even video scripts while maintaining brand voice and messaging consistency.

AI agents don’t just create content—they optimize it. They can analyze which headlines perform best, adjust content length based on platform requirements, and even adapt tone and style for different audience segments. Some agents can manage entire content calendars, ensuring consistent posting schedules while adapting to trending topics or current events.

AI Agents in Action: How PUMA Automated Ad Creation

Puma collaborated with agency Monks to create an experimental, fully AI-generated ad using hundreds of autonomous AI agents, marking a significant shift in how brands might approach creative development. Over five weeks, agents handled everything from concept ideation and scriptwriting to video generation, with minimal human oversight. Each agent was trained for specific tasks and audiences—for instance, curating ideas for Gen Z versus older demographics. The project demonstrates how agentic AI, unlike prompt-based generative AI, can automate entire workflows. Although the resulting one-minute spot had some AI glitches (like extra fingers), it serves as a prototype for scalable, AI-driven ad pipelines. Monks plans to extend this agentic approach to other clients like Google and BMW, and is building infrastructure with Nvidia to support broader adoption across the industry.

Social Media Management

Social media marketing demands constant attention and quick responses, making it ideal for AI agent assistance. These systems can monitor brand mentions across platforms, engage with customers using natural language, and escalate complex issues to human team members when necessary.

AI agents analyze social media conversations to identify emerging trends, track competitor activity, and gauge public sentiment about brand initiatives. They can automatically respond to common customer inquiries while maintaining brand personality and voice guidelines established by marketing teams.

This capability extends into influencer marketing and partnership management, where identifying and managing relationships with influencers and brand partners can be time-consuming. AI agents can analyze social media metrics, audience demographics, and content quality to identify potential partnership opportunities. They also monitor partnership performance and provide recommendations for optimizing collaborative marketing efforts, helping brands scale their influencer programs more effectively.

Customer Relationship Management

AI agents are transforming how brands manage customer relationships throughout the entire customer journey. These systems can identify high-value prospects, predict customer lifetime value, and determine the optimal communication frequency and channel for each individual customer.

By analyzing patterns in customer behavior, AI agents can predict when customers might be ready to make a purchase, likely to churn, or interested in upgraded products or services. This predictive capability allows marketing teams to be proactive rather than reactive in their customer engagement strategies.

AI Agents in Action:Vera Bradley and Microsoft Copilot

Vera Bradley revamped its customer service capabilities by integrating Microsoft Dynamics 365 with AI-powered tools like Copilot and AI agents. These enhancements enabled the brand to deliver faster, more personalized support and empower agents to handle customer inquiries more effectively. Features such as case summarization, email and chat drafting, and knowledge article suggestions helped reduce repetitive workloads and streamline resolution workflows. AI agents—configurable via Copilot Studio—can manage routine requests, escalate complex issues with full context, and prioritize high-value interactions for human agents.

Market Research and Analytics

Traditional market research often involves time-consuming surveys, focus groups, and data analysis. AI agents can continuously monitor market conditions, analyze competitor activities, and track consumer sentiment in real-time. They can process vast amounts of unstructured data from social media, review sites, and news sources to identify emerging market opportunities or potential brand risks.

These systems can automatically generate market research reports, track key performance indicators across multiple channels, and provide actionable insights that help marketing teams make data-driven decisions quickly.

AI agents in action: Protecting LUSH products against counterfeits

Lush partnered with MarqVision to combat a surge of sophisticated counterfeiters and impersonators selling expired or unpackaged products online, which threatened customer safety and brand trust. Given the difficulty of enforcing rights on “naked” products without packaging or serial numbers, Lush used MarqVision’s AI-driven enforcement tools to tackle infringements across trademarks, copyrights, and regulatory violations. Within one month, they removed over US$400,000 worth of illegal listings, shut down fake “Lush Official Stores” on Shopee, and achieved a 100% takedown rate for detected impersonators—safeguarding both customers and brand integrity.

AI Agents Transforming Consumer Shopping

Walmart’s Super Sparky (also more at the WSJ)

Walmart is using advanced AI “super agents” to make online shopping smarter and more efficient—for both customers and employees. Instead of using different apps or tools for every task, Walmart has created four AI agents that each focus on a specific group: one helps customers with things like tracking orders or finding recipes based on what’s in their fridge, another supports store workers with tasks like checking schedules, a third helps suppliers manage orders and ads, and a fourth assists Walmart’s tech teams in building new AI tools.

These AI agents can work together and do many tasks automatically, saving time and making things run more smoothly. Walmart hopes this will help boost online sales and improve customer service. Even though the company says these tools will create new jobs, it’s also investing in automation—which raises questions about how AI might affect future employment. For students studying business, marketing, or tech, this is a real-world example of how companies are using AI agents to transform how they operate and serve people.

Product Discovery and Research

AI agents are revolutionizing how consumers discover and research products. Instead of starting with traditional search engines like Google and clicking through multiple websites, consumers can now ask AI agents comprehensive questions and receive synthesized recommendations from multiple sources.

These digital shopping assistants can understand complex, conversational requests like “I need a sustainable winter jacket for hiking that’s under $200 and available in petite sizes.” The agent analyzes product specifications, customer reviews, expert opinions, and availability across multiple retailers to provide personalized recommendations that would take humans hours to research manually.

Unlike traditional product search that requires consumers to know specific brands or product categories, AI agents can interpret needs-based queries and suggest solutions the consumer might never have considered. They can also explain trade-offs between different options, helping consumers understand why one product might be worth a higher price or why a lesser-known brand might better meet their specific requirements.

Personalized Shopping Experiences

AI shopping agents create highly customized experiences by learning from each consumer’s preferences, purchase history, and stated needs. These systems remember previous conversations and can build detailed profiles of individual shopping patterns, budget constraints, and lifestyle requirements.

For example, a consumer planning a vacation might work with an AI agent over several weeks, gradually refining travel options as the agent learns about family preferences, budget flexibility, and must-have experiences. The agent can adapt recommendations in real-time as circumstances change, such as adjusting suggestions when flight prices fluctuate or weather forecasts shift.

These agents excel at managing complex, multi-step purchases that involve numerous decisions and comparisons. Planning a home renovation, choosing technology ecosystems, or selecting business services often requires evaluating dozens of variables simultaneously—exactly the type of task where AI agents provide significant value over traditional shopping methods.

Price and Value Optimization

Consumer AI agents can continuously monitor prices across multiple retailers, track historical price trends, and predict future discounts. They can automatically alert consumers when desired items go on sale or suggest optimal timing for purchases based on seasonal patterns and inventory cycles.

More importantly, these agents can evaluate total cost of ownership, not just upfront prices. When comparing products, they can factor in durability, maintenance costs, energy efficiency, and resale value to help consumers make financially optimal decisions over the long term.

Purchase Execution and Management

AI agents are beginning to handle the actual transaction process, moving beyond recommendations to completing purchases on behalf of consumers. They can compare shipping options, apply available discounts and coupons, and ensure optimal delivery timing based on the consumer’s schedule.

Some agents can manage subscription services, automatically reordering consumable products when supplies run low, or adjusting service levels based on usage patterns. They can also handle returns and exchanges, comparing policies across retailers to ensure consumers get the best post-purchase support.

Marketplace Navigation and Trust Assessment

One of the most valuable consumer applications is helping navigate the overwhelming number of online retailers and marketplace sellers. AI agents can assess retailer reliability, return policies, customer service quality, and delivery performance to help consumers choose not just the right product, but the right place to buy it.

This is particularly valuable for consumers who previously limited themselves to familiar retailers like Amazon or major brands because researching every seller was too time-consuming. AI agents can safely expand consumer choice by vetting smaller retailers and specialty sellers that might offer better products or prices.

Budget Management and Financial Planning

Advanced consumer AI agents can integrate with personal finance tools to help consumers make purchase decisions within their budget constraints and financial goals. They can suggest alternative products that better fit spending limits, recommend timing purchases around income schedules, or identify when splurging on quality might save money long-term.

These agents can also track spending across categories, identify opportunities to save money through bulk purchasing or subscription optimization, and help consumers understand the financial impact of different lifestyle choices.

The key difference from marketer-focused AI agents is that consumer agents prioritize individual benefit optimization rather than business metrics, focusing on finding the best value, quality, and experience for each person’s unique circumstances and preferences.