The agentic AI implementation guide for customer experience
Customer expectations for 24/7 personalized service are on the rise. But for customer experience (CX) and support teams weighed down by high inquiry volumes and staff shortages, meeting these evolving demands remains an uphill battle.
Agentic AI—autonomous AI agents that operate on behalf of customers and businesses—is seeing widespread implementation across industries because it offers a solution to these persistent challenges.
Unlike traditional automation or chatbots that only respond when prompted, AI agents operate with a degree of autonomy. They can reason, direct their own processes and tool usage, and execute tasks from start to finish across systems. By automating tasks that once required human intelligence, agentic AI helps teams do more with less, while handily meeting customer expectations.
However, to ensure a successful agentic AI implementation, you’ll need to do it right. According to a recent Rand report, over 80% of AI projects fail—which is double the failure rate of non-AI tech projects.
“The autonomy that makes AI agents so promising as a business solution is also what makes it tough to implement them safely and with confidence.”
— Shailesh Nalawadi, Head of Agentic AI Product Management at Sendbird
The best implementations are built on a solid AI strategy that lays out how they’ll be designed, deployed, and scaled. But you don’t need to figure this out on your own.
In this guide, we’ll show you how to successfully implement agentic AI in CX and customer support operations—from assessing readiness to scaling AI with confidence—so you can unlock the benefits of this powerful new technology.
Build lasting customer trust with reliable AI agents
What is agentic AI for CX?
Agentic AI in CX refers to autonomous AI systems—often called AI agents—that can reason, understand intent, use tools, and act on behalf of businesses or customers in pursuit of predefined goals.
Rather than just producing a static response like a basic chatbot, AI agents act like digital employees. They connect to backend systems, execute multi-step processes, adapt to changing inputs, and operate seamlessly across channels—all with little or no human intervention.
By anticipating customer needs and acting proactively, they create a more personalized experience for customers and new efficiencies for organizations, while freeing human agents to focus on more nuanced, customer-centric tasks.
For example, an AI agent for customer experience can resolve a “Where’s my order?” inquiry on its own by pulling data from CRMs, ticketing systems, and knowledge bases. It can also switch channels seamlessly, going from email to SMS to in-app, for a continuous journey that includes a context-rich handoff whenever necessary.
Understanding agentic AI in CX
Support and CX leaders are increasingly turning to agentic AI to handle tasks that once required human intelligence. According to recent CISCO research, by 2028, a full 68% of all customer service and support interactions with technology vendors are expected to be handled by agentic AI.
Unlike previous tools that rely on rigid rules or user prompting, agentic AI works in a continuous loop of observation, reasoning, and action. It perceives context, weighs options, and executes tasks autonomously—much like a human—but with the speed, precision, and consistency of a machine.
Offering a mix of goal-oriented behavior, context-awareness, and real-time data access, agentic AI introduces a new level of adaptability and omnichannel coordination to CX operations. By integrating seamlessly across platforms, they unify fragmented touchpoints into one smooth, deeply personalized journey that’s most satisfying for customers and more efficient for businesses.
The core technologies driving agentic AI in retail include:
Large language models (LLMs): The "brain.” Processes natural language inputs, generates responses, and enables the agent to engage in intelligent, fluid dialogue.
Natural language processing (NLP): Enables agents to interpret and understand nuanced human language, recognize intent, identify entities, and perceive sentiment.
Machine learning (ML): Agents can learn from interactions, adapt to new information, and continuously improve over time with ML.
Memory systems: Agentic AI needs the ability to retain context across interactions, in both short- and long-term memory, to deliver the best CX.
Tool use and API integration: Enables agents to interact with external tools and systems like CRMs, ticketing, and databases to complete tasks.
Businesses can use agentic AI to improve their customer experience and support in many ways. Here are a few core use cases of agentic AI in CX we’re seeing at Sendbird:
Hyper-personalization: AI agents analyze both historical data and current context to offer tailored solutions, guide product discovery, and optimize marketing promotions in the moment to drive engagement, conversion, and satisfaction.
Proactive issue resolution: By monitoring user data and interactions, agents can predict issues like billing errors and trigger proactive solutions before a customer reaches out to reduce ticket volume.
Instant 24/7 omnichannel support: By integrating across channels, agents can instantly resolve multi-step issues to reduce wait times and deliver the most consistent, seamless brand experience at every point.
Scalable, localized staffing: AI agents can scale across millions of interactions and be localized to different languages, contexts, and products without sacrificing tone or accuracy. This reduces temporary staff needs and upholds service quality in peak periods.
Benefits of agentic AI in CX
Integrating AI agents into core CX and support functions can have a significant advantage for business and their customers. For example:
Turning a cost center into a revenue driver: AI agents turn support interactions into growth opportunities by surfacing upsells, cross-sells, and loyalty offers in service interactions at contextually relevant moments.
Unified customer profile & insights: AI agents gather all interaction data into a single, evolving customer profile to keep CX smooth across channels and drive data-driven decisions.
Efficiency and cost savings: They automate repetitive tasks, intelligently triage inquiries, and keep SLAs intact during seasonal surges to reduce ticket volume without adding to headcount.
Higher CSAT scores: With faster issue resolutions, 24/7 availability, and hyper-personalized support, agentic AI creates the kind of seamless experiences that build loyalty and customer satisfaction.
How to implement AI agents in CX
Ready to start using AI in customer experience and support? Here are 10 steps to follow as you consider how to approach your agentic AI implementation:
1. Assess your business needs
The first step is to identify areas of your support operations and CX that will benefit most from agentic AI integration. The advice of Shailesh Nalawadi, Head of Agentic AI Product Management at Sendbird, is this:
“The safe bet is to start with an AI pilot for a high-value but narrow use case. Once you nail that application, you move on with more confidence and more data. It’s about taking small steps and setting up for future success.”
Analyze your current processes, customer pain points, and challenges to identify the right use case. Where are the inefficiencies? Where can AI reduce resolution times? Enable self-service? This will give you a sense of what’s ripe for automation, and where agentic AI implementation can deliver measurable ROI
2. Start with a repeatable task
Once you’ve identified your needs and objectives, it’s best to start with a use case that’s simple, practical, impactful—maybe even a little boring. Agentic AI thrives on consistency and repeatability, so look for narrowly defined, repetitive tasks that eat up time for human agents or create friction for customers.
“For instance, Hertz Rent-A-Car built an AI agent just to help customers extend their rental period. That’s it.” says Nalawadi. “At the start, the agent was resolving maybe six out of 10 cases correctly. With tuning, that went to seven in 10, and eventually eight in 10. Even if you stop there, the efficiency gains are significant and compound over time—so long as the ROI is there to start.”
By choosing a focused use case suited to autonomous decision-making, but that doesn’t exceed AI’s current limits, you set yourself up for measurable success. And as agentic AI evolves, these early wins are your foundation for scaling to more complex agentic AI workflows.
3. Evaluate your data
Agentic AI is only as good as the data it runs on, meaning that poor data quality is sure to lead to poor outcomes that erode trust with customers and internal teams. This makes good data management practices essential before and during implementation. Agentic AI has the unique ability to process structured as well as unstructured data, such as product catalogs and chat transcripts, and the same principles apply here.
If you’re deploying an AI customer service agent for ticket triage—a common early use case thanks to its clear ROI—you’ll need to ensure customer data, knowledge base content, and tool integrations are accurate, secure, and accessible. Otherwise, the agent will deliver inconsistent or inaccurate answers, frustrating customers rather than resolving issues at first contact.
Pro tip: You don’t need to let “perfect data” delay your launch. Rather than insisting upon 100% clean structured data, you can refine your data sets as your implementation scales.
4. Develop a plan
Once you’ve assessed your organization’s readiness and goals, the next step is to create a clear agentic AI implementation plan. Map out timelines, budgets, and resource needs, while outlining how your AI agent will integrate into existing systems. This includes considering data migration, system configurations, and any potential disruptions to current workflows.
Also, research AI agent platforms to find the best fit for your business. Evaluate factors like integration capabilities, scalability, security, and ease of use. Customer reviews and proven results can also help guide your selection.
With Sendbird, you don’t have to start from scratch. Our easy-to-use AI agent platform helps you build, test, and scale AI agents across channels with a foundation of enterprise-grade infrastructure that’s trusted by 4,000 global businesses and handles 7 billion conversations each month.
How to choose an AI agent platform that works
5. Define your agent’s role
The next step is to design your AI agent by giving it clear instructions for its workflow. Whether the agent acts as a support concierge, escalation manager, or proactive account monitor, it needs structured AI prompts and access to the right knowledge sources and take appropriate action.
Returning to the example of Hertz’s rental extension, the workflow would be broken into a step-by-step set of AI prompts that would look something like this:
Validate customer authorization
“Check that the customer is logged in, authenticated, and has permission to modify this booking.”Review reservation details
“Pull up all existing reservations for this account. Confirm which booking the customer wants to extend.”Confirm eligibility
“Check availability for the same car type and location for the requested extension period.”Process the extension
“Update the booking in the reservation system and sync changes with the internal platform via API.”Communicate updates to the customer
“Confirm the new return date and pricing with the customer in natural, conversational language. Offer add-ons (insurance, upgrades) if relevant.”
“Equipping the agent with precise task instructions and contextual data is the key to ensuring it says the right things at the right time—and takes the right actions without human intervention,” says Nalawadi.
6. Define the guardrails
While it’s critical to specify exactly what you want your agent to do, it’s just as important to tell it exactly what not to do. This can involve defining the rules for escalation, compliance boundaries, and tone guidelines in your prompt.
If you’re building an AI agent with Sendbird, you get a robust suite of built-in guardrails as part of our Trust OS AI agent framework. By monitoring for hallucinations and errors in real time, these features help CX teams to ensure secure, compliant, and responsible AI use by keeping humans in the loop and ready to act instantly.
7. Integrate and test
Even the most targeted implementation will fall short if the agent doesn’t work seamlessly with your existing systems. Integration can involve connecting to CRMs, ticketing platforms, and core business tools through APIs and data syncing, then validating those connections in a controlled pre-production environment. Conduct pilot tests to address any integration issues before full-scale implementation to identify and resolve potential problems with accuracy, security, or compliance before going live.
“Unlike traditional IT, AI agents have autonomy, which can lead to unexpected outputs,” says Nalawadi. “When it comes to ensuring enterprise readiness of these new and evolving systems, there’s simply more required to make sure things are connected—they also need to be proven responsible.”
For example, Lotte Homeshopping, a leading Korean retailer, developed a rigorous AI agent testing protocol for its support agent, Moni. Beyond system compatibility, the company evaluated response accuracy, data protection safeguards, security, compliance, and tone consistency—ensuring the agent met the company’s exacting standards from day one.
5 key questions to vet an AI agent platform
8. Measure, optimize, and iterate
Just as you wouldn’t hire a junior employee and leave them unsupervised, AI agents also require ongoing training and evaluation after implementation. Establishing a structured process of continuous improvement will help ensure your agents stay accurate, accountable, and aligned with customer expectations.
Monitor KPIs like resolution time and CSAT to see how your AI agents are performing, and how customers are responding. For more granular insights, AI agent activity logs provide visibility into which interactions led directly to which outcomes, offering clear insights into how you retrain and fine-tune behaviors and improve performance.
Say you determined that your agent is asking too many repetitive questions before escalating. You could refine the handoff criteria so the agent recognizes key context sooner and escalates faster to reliably improve customer satisfaction.
9. Maintain trust in AI
The customer experience lives (or dies ) based on trust, so an agentic AI implementation must maintain and, ideally, enhance it. However, the more agency you give AI agents, the more it takes to ensure their trustworthy operations, both among customers and internal stakeholders. This is why the best agentic AI platforms come with built-in features to ensure AI is transparent, compliant, and accountable at all times.
In this context, here’s what to design for in AI agents:
AI observability: Teams need visibility into agents’ problem-solving processes in real time to detect and address issues, ensure accurate responses and proper tool usage, and improve performance.
AI governance and control: To move fast without compromising trust and safety, teams need the ability to manage changes to agent logic and deployments with precision.
AI responsibility: A core tension in agentic AI is balancing its autonomy with human oversight. In high-stakes situations or sensitive scenarios, humans should be in the loop or at hand for approvals to ensure good outcomes.
10. Prepare to scale
Once your pilot proves successful, the next challenge is scaling. It’s smart to expand your AI capabilities step-by-step, investing strategically to grow ROI while also keeping agentic AI implementation manageable.
For example, a retail wholesaler launched a Sendbird AI agent to assist in-store shoppers and guide them through product discovery in its mobile app. After initial success, the team added new workflows so the agent could proactively nudge customers with membership offers at the right moment—turning routine support interactions into growth opportunities.
Enterprise-grade infrastructure is critical at this stage. Without the near-zero latency that comes from proven scalability, agents will struggle to sync customer profiles across channels to deliver consistent, timely support at busy periods—undercutting their value and eroding CX.
The retailer appreciated how Sendbird AI agents scale seamlessly across languages, product lines, and contexts, maintaining context, accuracy, and brand voice without sacrificing security, compliance, or performance.
8 major support hassles solved with AI agents
Need help with agentic AI implementation? Partner with Sendbird
A successful agentic AI implementation opens the door to a truly proactive, personalized, and omnichannel customer experience. For customers, this means a consistent, concierge-style service across channels. For businesses, it means lower costs, greater efficiency, and improved customer loyalty and satisfaction scores.
Even if your team feels it has all the training, tools, and support it needs to launch an AI agent by itself, new research shows that having expert partners can help launch AI agents up to 16x faster.
“Rome wasn’t built in a day—and it wasn’t built alone. A lot of what we do at Sendbird with our Trust OS is help enterprises overcome the hurdles of trust, safety, security, and scalability around agentic AI.”
- Sheilesh Nalawadi, Head of Agentic AI Product Management at Sendbird
To learn how Sendbird can help you build, deploy, and scale AI agents you can rely on, contact sales or request a demo.
