Generative AI has been around for a few years now, transforming how we learn, write, code, and complete analytical tasks. More recently, tools offering AI-powered customer service and automation have flooded the market. These solutions promise to handle all those pesky, repetitive questions and tasks that ecommerce customer service agents often get overwhelmed with, allowing them to focus on providing better support for more complex issues and VIP customers.

These tools, however, are being eclipsed by a newer, more complex offering: agentic AI. Now, online retailers can deploy intelligent virtual agents that not only answer questions but also reason, act, and improve over time. These agents aim to deflect tickets, help you scale your business, cut support costs, and keep both customers and employees happy.

In this article, we’ll explore how agentic AI differs from the basic chatbots you’ve seen before, how a dual-agent strategy (one for customers, one for employees) works in real ecommerce environments, and what it takes to implement a tech stack capable of delivering 24/7 support that actually solves problems and can act autonomously on your behalf.

What is Agentic AI in Customer Service?

There’s no shortage of LLM-based customer service bots on the market. These tools can learn from your company’s knowledge base articles and documentation to accurately respond to common customer queries and complete actions, such as cancelling an order, on your behalf. Agentic AI assistants, however, take this concept to the next level. 

Agentic AI agents differ from these traditional AI assistants/chatbots in their ability to retain memory across sessions, reason independently, and initiate actions. They process customer and employee inquiries, discerning intent and context, and can pose clarifying questions when more information is needed.

Agentic assistants can handle customer inquiries, perform sentiment analysis, build customer relationships, and improve customer satisfaction, all while independently performing complex tasks–something that your conventional LLM/AI tools simply can’t do on their own.

Why Customer Service Needs an AI Transformation

Customers expect help on any channel at any time. In fact, 74% of customers have used multiple channels to start and finish a transaction, and 76% expect consistent interactions across those channels. 

They also increasingly prefer self-service options. 81% of consumers would rather use self-service for simple issues. This means an omnichannel AI solution is key to meeting customers where they are and providing instant, correct answers consistently.

On the customer service agent side, professionals are experiencing increasing burnout due to the high volume and often monotonous nature of customer requests. When one side of an interaction expects 24/7 personalized experiences across channels, and the other side struggles to keep up with this demand, something has to change. 

We haven’t even hit on the cost and operational efficiency that an agentic solution can provide a business. Handling inquiries digitally through AI is significantly cheaper than providing live service, while call deflection and automated chat can trim operational costs. 

It’s also cheaper to keep a few highly trained and experienced human agents on staff, rather than paying dozens of entry-level call center agents to handle the same handful of questions, day after day.  

The Dual-Agent Strategy: Two AI Assistants for Two Audiences

When people think of agentic AI, they typically envision conversational AI for customer support. Specifically, bots that mimic human agents in their ability to handle customer conversations and perform actions on behalf of a human. Still, there’s a whole other aspect to agentic AI that we haven’t discussed yet: the associate-facing agent. 

Here’s how both styles of AI models/assistants differ and can work together to resolve complex issues for your business. 

Consumer-Facing Agent: 24/7 Customer Support on Every Channel

This is the public chatbot/voice virtual assistant that interacts with consumers on your website, mobile app, social media, or phone line. It’s trained to handle customer inquiries, from product searches to order tracking. 

It must be polite, helpful, and able to guide the shopper. It also needs built-in safeguards (for example, it shouldn’t expose sensitive info) and have the ability to escalate to a human agent on request or when it reaches its limits.

If a question is too complex or if the customer is frustrated, the AI will smoothly hand off the conversation to a live agent. Using an integrated helpdesk, the bot can create a ticket or transfer the chat, while carrying over the conversation context.

This agent can be accessible on your website chat, mobile app, Facebook Messenger, WhatsApp, and even via voice on your phone support line. Customers today use multiple touchpoints, and they expect a seamless experience across all of them. 

Delivering a true omnichannel chatbot means a customer could start chatting on your site and later call by phone. The virtual agent should handle both interactions consistently, remembering the customer and the issue they were initially reaching out about.

Through integration with back-end systems, the bot can perform live lookups for things like order status, shipping updates, inventory counts, or account info. For example, a customer can ask, “Where is my order?” and the bot can fetch the latest tracking info from your Order Management System (OMS) and respond with the date and status.

Associate-Facing Agent: Empowering Your Store & Support Teams

This is an internal AI assistant embedded within the tools your team uses, such as your POS or CRM system. It’s like giving each employee a helper bot: they can ask it questions about products, inventory, or policies and get instant answers. The agent can access confidential internal data, such as stock levels, customer data/order history, or internal Standard Operating Procedure (SOP) documents that you wouldn’t want to expose to customers.

Its personality can be more direct and efficient, since it’s for trained staff. By separating it from the customer bot, you ensure security (customers can’t indirectly query internal data) and can tailor its knowledge base to employee needs.

Because this agent is internal, it can be granted access to systems like your helpdesk or OMS for customer profiles, order histories, etc. For example, a support rep on the phone could quickly ask, “When was the last order from customer X, and is there any open ticket?” and get that info without manually searching.

New or even experienced employees often need to check how to perform certain tasks or pull up rules around something such as return policies, warranty processes, etc. The AI agent can be trained on internal documents and policy manuals. An employee could ask, “How do I process an exchange for an online order?” and the AI can list the steps out in seconds, saving tons of time searching through PDFs and internal libraries. 

Using these two styles of agentic AI chatbots in tandem ensures that you have better overall security and performance. Now, your consumer agent is operating on one layer of data, while your associate agent is on an entirely different layer, available to assist human agents when consumer bots hand off complex calls and chats to them. 

A single bot trying to do it all would either be too limited for customers or too insecure for internal use. In the long run, this modular approach makes scaling and improving the system easier.

Architectural Approach: Conversational Agentic AI Tech Stack

If you’re considering agentic AI for your ecommerce business, it can be difficult to know where to start. The concepts are rather simple, but there are many different tools to implement and ways to achieve your goals. 

Here is our tech stack recommendation for a business looking for agentic AI to solve complex problems, handle customer interactions/routine tasks, and improve via machine learning and the conversations it has with your staff and customers. 

Conversational AI: Google Dialogflow CX (Google Conversational Agents)

Part of Google’s Customer Engagement Suite, Google Dialogflow CX (now rebranded as Conversational Agents) is the “brain” of both virtual agents (for customers and associates). It’s Google Cloud’s advanced conversational AI platform for building chatbots and voice bots. We like Dialogflow CX because it supports complex conversation designs (with stateful flows and context), multi-language support, and it integrates well with other Google services. 

Essentially, Google Conversational Agents handles the Natural Language Understanding (NLU) of your stack. It takes user inputs via text or voice, interprets the intent, and manages the dialogue flow. Each agent will be a separate project, allowing you to design custom intents, flows, and responses tailored to each audience.

Knowledge Integration: Google Vertex AI Search

Factual answers are only as good as the data you feed the AI. For answering knowledge-based questions, such as product details or policy information, we suggest Google Vertex AI Search, a powerful enterprise search that leverages Retrieval Augmented Generation (RAG). 

Vertex AI will ingest relevant documents, such as product spec sheets, user manuals, support FAQs, and internal policy PDFs, into its data stores. When a Dialogflow Conversational Agent receives a question that’s not a predefined intent, it can query Vertex AI Search to retrieve the most relevant pieces of text, and then generate a concise answer based on those, thus grounding the answer in real data. This approach ensures that the AI’s answers come from your company’s actual knowledge, rather than the AI confidently “making up” or hallucinating an answer. 

For example, if a customer asks a very specific compatibility question, the agent can pull the info from a product manual PDF via Vertex Search and answer correctly. Likewise, the internal agent can use this to pull information from SOP documents to guide an associate. Simply put, Vertex AI Search is your knowledge base Q&A engine.

Backend Integrations: Google Cloud Run Functions and APIs

Many queries, such as checking order status, querying inventory, or creating a support ticket, require live data or actions. For this, we suggest using serverless functions with Google Cloud Run Functions as middleware. Conversational Agents can trigger a webhook fulfillment that calls a Cloud Run Function, and that function will call the relevant external API or database. 

The Cloud Run Function then returns the result to Dialogflow, which the agent uses to respond. This setup is modular and secure: each integration is a separate function, keeping the logic organized and easy to manage. Because it’s serverless, it scales automatically when many requests come in, and you only pay per use, which is a great cost benefit.

The Three Pillars of an Agentic Customer Service Strategy

Once you have the technical aspect of your setup worked out, there are a few key considerations to keep in mind regarding your implementation strategy. 

Balancing Real-Time Data and Performance: A Hybrid Data Strategy

One aspect of this AI-driven architecture is how it handles data for the agents. When building a solution like this, you need to consider how to balance accuracy with the speed and reliability with which your setup resolves issues. This is why we recommend a hybrid approach:

• Live API calls for real-time data: Some information changes by the minute and must be fetched fresh. For example, an order’s shipping status, current stock levels, or a customer’s up-to-the-minute loyalty points balance. For these, the AI should make real-time calls to the respective systems whenever the question is asked. This guarantees 100% accuracy at that moment. The tradeoff here is a slightly slower response time than with static data. 

• Pre-loaded knowledge bases for static data/large databases: A lot of information is relatively static or can at least be cached for more extended periods. For this data, you can use Vertex AI Search to index the information in advance. By doing this, the AI agent can retrieve answers from a fast search index rather than hitting the live database each time. It improves performance and ensures the bot can function even if one of the backend systems has a hiccup, as it has a cached knowledge store to draw upon.

By hybridizing data access like this, you can ensure that the AI is both fast and accurate. Customers receive quick answers to general questions because the information is pre-indexed, and they get up-to-date answers to specific status queries because they can be called live. 

Seamless Human Handoff: Blending AI with Live Support

No AI system can (or should) handle 100% of customer inquiries. There will always be edge cases or customers who prefer — or need — a human touch. The success of your agentic AI project hinges on making the AI-to-human handoff smooth and intelligent. Here’s how to address that:

• Built-in escalation paths: Every conversation flow in the Dialogflow CX agents should include checkpoints or triggers for escalation. For the customer-facing bot, this could be when the user explicitly says “agent,” “real person,” or “representative”, or when the AI detects frustration, for example.

• Transfer with full context: When the bot transfers a chat or call, it should include a transcript of the conversation up to that point and any relevant details. For voice calls, if integrated with a system like Freshdesk or another CCaaS, the agent might see a screen pop with the call info and a text summary of the bot interaction. This way, the customer never has to repeat information. 

• Fail-safe to traditional channels: If, for some reason, no live agent is available–for example, it’s after hours, or all agents are busy–and the customer insists on human help, the bot can offer alternatives like asking if the customer would like a support ticket created or a callback scheduled.

• Training the AI on handoff cases: Over time, analyze the conversations that led to handoff as part of continuous improvement. If you notice trends, such as many people asking about a new issue that the bot didn’t handle, train the bot to address that next time. The goal is to reduce unnecessary handoffs by continually expanding the AI’s knowledge and capabilities. However, you should never aim for 100% deflection. Complex or sensitive issues, such as billing disputes or angry customers, are often better handled by a person. The aim is to reach a sweet spot by possibly deflecting 60–80% of chats and ~50% of calls to self-service, based on industry benchmarks for well-implemented solutions. We’ll talk more about the handoff in a bit. 

• Customer assurance and transparency: Your bot should always be transparent that it’s an AI. When handing off to a CS agent, it should politely explain that a human is coming on. This transparency builds trust. Customers are generally fine with AI help if it works, but they appreciate clarity.

This approach treats the AI agents and human agents as a collaborative team. The AI should know what it can and can’t handle, and know when it’s appropriate to pull in a human colleague. 

Continuous Improvement: Self-Learning Systems and Optimization

One of the advantages of AI-driven support is that it can learn and improve over time. In implementing an agentic solution like this, you are establishing a process and utilizing tools to ensure the bots become smarter and more accurate every day.

As we mentioned in the previous section, you should train your bot on past cases where it needed to hand off the conversation to a live agent. Recorded conversation logs can help you find common points of failure or gaps. For example, if you see multiple customers asking about “model Z compatibility with product Y” and the bot didn’t have a good answer, that’s a sign to add that info to the knowledge base or create a new dialogue flow.

Beyond manual review, you can even use AI to analyze AI. Google’s Dialogflow Conversation Agents and other tools provide analytics dashboards that display metrics such as intent match rates, frequently unanswered questions, and user satisfaction scores to help pinpoint areas where the bots may be providing less-than-desirable responses. 

With the above inputs, you can run iteration cycles, similar to agile development, but tailored for AI content: updating training data, adding new intents, adjusting integration logic as needed, etc. Over time, your AI should steadily handle a higher share of interactions with increasing accuracy and satisfaction.

Is Agentic AI Right for Your Business?

Agentic AI can accomplish incredible things, but it’s not for everyone, primarily due to the costs involved. Getting set up with this kind of solution will cost hundreds of thousands of dollars, as using this kind of computational power isn’t cheap, nor is it quick to integrate. 

You’ll need to assess exactly what you are aiming to achieve and compare it to the usage costs of all these tools, as well as how much you’ll need to pay for the initial setup. Many of these solutions require numerous hours of consulting, development, configuration, setup, and iteration. 

If you have a small team, this might not be for you. However, if you have ~3 or more full-time support staff, that’s a point where AI could likely reduce one or two human jobs’ worth of work, making it worth the investment.

Also consider your growth plans. Are you expecting your customer base or product catalogue to grow? Implementing an AI solution now will position you to handle growth without incurring excessive support costs. 

Finally, if your products require a lot of explanation or have detailed specs, an AI agent can really help by storing all that knowledge and retrieving it instantly.

The best way to determine whether this kind of solution will work for you is to consult with an agency like Blue Badger, which can analyze your existing setup and tools to develop a plan of action and a recommended stack to achieve the best ROI and overall results. 

If specific prerequisites aren’t met, such as a lack of knowledge base content to feed the bot or the absence of APIs in your systems, an agency can highlight these and include steps to address them as well. 

Expected ROI and Benefits for Ecommerce Businesses

Investing in an agentic AI-driven customer service transformation is a big decision, so what’s the payoff? For CEOs and decision-makers, the benefits span both quantitative ROI (cost savings, conversion lift) and qualitative improvements (customer and employee satisfaction). Here’s what you can expect:

1. Cost Savings and Efficiency Gains: By automating a large portion of inquiries, you can handle a higher volume with fewer agents. For example, if you currently have a team of 5 handling 1,000 contacts/week, after the AI agents are in place, you can handle the same volume with 2 - 3 humans, as the AI deflects the rest. That’s hundreds of thousands of dollars saved in salaries each year. 

2. Higher Customer Satisfaction (CSAT) and Loyalty: Faster responses and 24/7 help naturally lead to happier customers. No one enjoys waiting or jumping through hoops for basic info. With AI handling things instantly, you remove friction.

3. Increased Conversion Rates: Especially in ecommerce, a well-handled inquiry can be the difference between a sale and an abandoned cart. The customer-facing agent can engage shoppers who might otherwise leave by answering product questions, guiding them to the right item, or offering to track a package. By being available 24/7, you might snag orders from late-night browsers that you’d lose if they had a question and no one to ask. 

4. Scalability for Peak Times: If your business has seasonal spikes or occasional viral surges, AI agents can handle sudden influxes of inquiries without the need to rapidly hire/temp staff, which is expensive and often impractical.

5. Happier, More Productive Human Agents: Your team members will likely feel the difference. The associate-facing agent reduces the time they spend looking up information, allowing them to serve customers more efficiently. When agents aren’t bogged down with trivial questions, they can focus on complex issues, which is more rewarding work.

Simply put, agentic solutions lead to tons of money saved, faster service, and happier employees and customers overall. 

Conclusion

Agentic AI represents a shift in how ecommerce brands can deliver customer service at scale. By deploying purpose-built virtual agents for both your customers and internal teams, you can provide faster, more consistent support while reducing operational costs and minimizing agent burnout.

This isn't just a plug-and-play endeavour, however. It takes planning, the right tech stack, and guidance from experts who have the skills and experience to know what will work for you and what’s best left out. 

At Blue Badger, we have the skills you need to get set up with agentic AI agents and workflows. Contact us today to learn more about this solution or to build an implementation strategy that’s tailored to your business.