AI Customer Experience in 2025: Winning with Agents, MCPs & RAG

TL;DR

AI Customer Experience is the use of AI to guide, support, and advise customers across every touchpoint—spanning pre-sales (sales, marketing, documentation) to post-sales (support, service, solutions)—delivering help that is scalable, fast, and personalized.

• 2025 marks the shift from build → buy: Enterprises now favor mature third-party AI CX platforms over custom in-house builds.
• RAG is the foundation: LLMs rooted in company data, reduced hallucinations and is enabling reliable support at scale.
• Agentic AI + MCPs are game-changers: AI can plan the steps, work across your tools, and wrap up tasks by itself.
• Beyond support: AI agents now power sales, docs, marketing, and product workflows—accelerating content creation, technical enablement, and analytics.
• Winners will blend developer + non-developer access: the best solutions empower both technical teams and business users with flexible orchestration (Inkeep's positioning).

With all the above, executives should expect to hear "we did" more than "we should" with AI Agents.

AI Customer Experience in 2025: An Inflection Point

2025 has been a breakout year for AI customer experience. For the first time, more companies are purchasing mature third-party AI than building their own. But things don't just end there, advancements in LLM context windows, voice AI and image recognition have unlocked a new level of reliability & modality for enterprise AI, where AI customer support increasingly becoming a winner use-case.

This combination of ever-advancing technology and expanding budgets is leaving many customer support decision-makers wondering how they can best benefit their company and team(s).

If you're evaluating how AI can enhance your customer experience for your organization, this guide covers:

• How AI customer experience technology evolved from chatbots to intelligent agents
• How RAG (finally) unlocked reliable, rooted AI responses on company products
• Why Agentic AI and MCP are game-changers for automation in customer experience

How AI Customer Experience Technology Evolved: From Decision Trees to LLMs

Until the introduction of Large Language Models (LLMs), AI customer support was constrained by rigid architectures.

Traditional customer chatbots relied on:

• Fixed decision trees mapping keywords to responses
• Pre-written scripts for common scenarios
• Simple integrations for basic tasks (order status, password resets)
• Extensive manual maintenance for any new query types

These customer service tools handled FAQ-style questions adequately but failed at the edges. Any question outside the predefined paths required human intervention, creating frustration for customers and inefficiency for support teams.

LLMs fundamentally changed this paradigm by introducing:

• Natural language understanding without rigid scripts
• Dynamic response generation based on context
• Ability to handle multi-turn conversations
• Reasoning capabilities for complex queries

However, early LLM implementations faced a critical challenge: hallucinations. Without proper grounding, LLMs would confidently generate plausible-sounding but incorrect information about company policies, features, or processes.

RAG: The Foundation for Reliable AI Customer Experience

Introduced in 2020 by Meta's AI research team, Retrieval-Augmented Generation (RAG) emerged as a solution to reduce LLM hallucination risks and ground their answers from a defined source of truth like a database.

At their core, RAG systems:

• Retrieve relevant content from knowledge bases, documentation, or databases
• Augment the LLM's context with this verified information
• Generate responses grounded in actual company data

In essence, RAG is a framework to ground LLMs to a single knowledge base/source of truth. Using RAG, LLMs would fetch relevant content from knowledge bases or databases and use it to formulate answers, thereby reducing hallucination and enabling more advanced help.

Pre-LLM Chatbot vs RAG Enabled LLMs

When RAG became popularized in 2023 as a framework to use with GPT-4, companies jumped at the opportunity to build their own RAG enabled AI chat systems. AI Customer Support in the form of enhanced chatbots was the obvious use case, and the "build versus buy" question began to emerge.

But not all RAG bots are equal & the cost of maintenance became a drain for many. Companies that chose to build their own customer support chatbots quickly began to realize the high cost of time associated with updating the customer support chatbot. This is where the "buy" argument steps in, and where startups like Inkeep began to emerge.

Beyond Chatbots: How RAG Unlocked New AI Use Cases

The advent of RAG not only boosted customer support bot accuracy, but it enabled CS bots to be a versatile tool that touched use cases and teams beyond customer support. The introduction of RAG expanded AI customer experience from a single-channel solution to a platform serving multiple teams in a variety of product-related situations.

New Use Cases, New Requirements

The expansion of enterprise AI platform applications beyond AI customer support through RAG's advent meant that LLMs needed to work with more tools.

At the time, LLMs were seen as highly skilled at conversational reasoning, but not acting outside the realm of chat. To resolve this, OpenAI had already introduced Function Calling to connect an LLM to a tool. From there, the topic of AI Agents began to rise. By pairing LLMs with tools, they can then be "orchestrated" to be put into a loop where an LLM reasons, then acts by calling tools, then observes the tool's output, and then repeats until it accomplishes a stated pre-defined goal.

This gave further credence to the rise of "AI Agents" — LLMs that get stuff done.

MCPs Step In

Connecting an AI model to tools required writing custom, one-off integration code. This approach was time-consuming and didn't scale to hundreds of tools.

To get around this, Anthropic open-sourced MCPs (Model Context Protocols) to create a common, open-standard to expose tools via MCPs. So instead of building custom integration all the time, developers can simply expose their tools via an MCP-compliant server. The protocol handles the "handshake" between the agent and the tool to enable the flow of data.

With MCPs now being able to be plugged into any enterprise data source or tool without one‑off integrations, the idea of Agentic AI entering the enterprise as a "teammate" really begins to rise.

Capability Comparison

AI Agents Lead to New Use-Cases Beyond Customer Support

With the rise of MCPs and AI Agents, we're seeing new capabilities for AI Customer Experience that go beyond simple chatbots or RAG-powered answer engines.

These AI Agents—orchestrated together, using MCPs to call tools, and grounded by RAG—create effective ways to automate work that would otherwise be tedious and time-consuming.

Examples by Team

Content teams like Docs & Marketing can deploy AI Agents to automatically scan help desks for recent resolutions and draft FAQs based on these findings. They can also create agents that review code changes from engineering and draft corresponding documentation updates.

Sales teams get expert AI assistants can answer technical questions similar to sales engineering and leverage call transcripts to automatically draft email follow-ups and CRM notes. This reduces instances of "I'll get back to you" when customers ask complex product questions, potentially accelerating sales cycles.

Product teams use conversational CS assistants to provide valuable customer analytics regarding conversations, feedback, and usage patterns. This enables automatic identification of common customer pain points, auto-drafted PRDs for feature requests, and priority signals based on actual data rather than opinions.

Support Teams leverage support copilots to directly assist human agents by drafting answers with citations from the company knowledge base, summarizing and categorizing tickets, suggesting actions and next steps, and connecting multiple systems that would otherwise require numerous clicks.

The Future of AI Agents: A Shift from Workflows to Teams

One the most significant directions of where we’re headed with AI Agents is shift of the underlying architecture that caters to specialization of tasks (”multi-agents”).

This is critical, because a multi-agent framework is a system that coordinates multiple agents, each with its own prompt, role , knowledge, or tools, to accomplish tasks more effectively with specialization. Rather than one “do-everything” agent, a multi-agent framework distributes responsibilities across specialized agents, following various design patterns.

But why bother with this complexity? Isn’t a single agent enough?

That’s because of 2 reasons: hallucination risks & context overload.

1. For hallucination risks, a single agent loaded with dozens of tools risks becoming indecisive or error-prone. The LangGraph team has observed that 5–10 tools is the sweet spot before performance degrades. Beyond that, the agent are prone to misfire, which wastes tokens and time.
2. For context overload: Instructions and context become tangled when a single agent tries to juggle conflicting directives.

Therefore, breaking work into specialized agents keeps instructions clear and avoids contradictory reasoning.

What does this mean: Think of it like organizing a team project. Multi-Agents works like a smart team where members can hand off tasks to each other or ask for help and get answers back—just like real teamwork. In contrast, workflow that incoporate single agents are more akin to an assembly line where each step happens one after another in a set order. The Multi-Agents approach is better for complex problems that need flexible thinking, we each agent in the ‘team’ can reason on its own.

The Graphic Below to get an illustrative note.

Understanding the Graphic: Team Collaboration vs Assembly Line

Think of the top flow-chart like a manufacturing assembly line or traditional workflow automation. Each step is predetermined: trigger → process data → run AI tool → check conditions → execute actions → move to next workflow. It's highly efficient for predictable, repeatable processes where you know the exact sequence beforehand.

• The key insight: workflows follow predetermined paths with conditional branching. While AI tools can be inserted into the workflow, the overall orchestration remains rule-based rather than intelligence-driven.

Think of the bottom flow-chart like a consulting team tackling a complex client problem. The Agent Router acts as a project manager, deciding which specialist should handle different aspects of the request. The Analysis Agent might identify key issues, then either hand off the entire case to the Research Agent (like transferring a client) or delegate a specific research task while staying in control (like asking a colleague for input).

• The key insight: agents make dynamic decisions about who should handle what, just like humans do. The Artifact System tracks every source and decision, creating a paper trail (crucial for compliance). Context Fetchers inject real-time data ({{variables}}) so agents always have current information, not static snapshots.

The big picture: We're in the middle of a shift from "Workflows" to "AI Teams." Early automation was about connecting apps and moving data from A to B to C. Now we're moving toward having entire AI teams that can work together, specialize in different areas, and collaborate just like human teams do. Companies that understand this shift early will have a major advantage in automating complex work that previously only humans could handle.

How to Evaluate Solutions for AI Agents

Incumbent solutions can be segmented into 2 separate groups:

1. Powerful, yet highly technical
2. No-code, but only surface-level

For executives looking to get ahead with AI agents, our recommendation is to follow a holistic based approach centered around equal empowerment of non-developers and developers alike.

Key Questions to Ask

1. Is this solution accessible to both developers and non-developers alike?
2. How do they integrate with our existing software?
3. Can you customize these Agents so they're brand aligned, trustworthy & safe?
4. Do they add extra load on our engineering teams?

Executive Decision Framework: What To Look When Partnering With AI Agent Providers

Inkeep's Approach: Enterprise AI Agents That Get Work Done

At Inkeep, empowerment is a core brand pillar. We live in a world where "we did" overcomes "we should", thanks to AI Agents.

Our vision is to make multi-agent creation accessible to all across an enterprise, especially those in CX — with no-code in an interoperable fashion with a pro-code approach so CX teams can work side-by-side with their engineering team, if necessary.

Built Your Way: Visual Builder Meets Developer Power

For business teams: Our Visual Builder makes sophisticated multi-agent systems accessible without code. CX managers can create, test, and deploy agent workflows in minutes, not months.

For technical teams: Our full-stack TypeScript SDK, UI Kit, MCP integration, and RAG framework enable deep customization while maintaining the visual overview that business stakeholders need.

This approach enables interoperability between business teams & technical teams. That's because everything translates seamlessly between visual and code—true "UI-to-code, code-to-UI" capability that lets teams collaborate instead of compete.

Enterprise-Ready From Day One

• Multi-tenancy with secure credential management across different product lines
• Comprehensive tracing for audit trails and performance optimization
• MCP integration for future-proof extensibility
• Rich UI components beyond text—interactive elements, data visualizations, and branded experiences

The Bottom Line: Inkeep doesn't just answer questions—it resolves issues with the reliability, attribution, and governance that enterprise CX demands.

If you're an enterprise looking to deploy AI agents then let's schedule a call.

Frequently Asked Questions

What is AI Customer Experience?

AI Customer Experience (CX) is the application of artificial intelligence to guide, support, and advise customers across every touchpoint—from pre-sales (marketing, sales, documentation) to post-sales (support, service). It delivers scalable, fast, and personalized help by leveraging technologies like LLMs, RAG, and AI agents.

How is AI CX different from traditional chatbots?

Traditional chatbots rely on fixed decision trees and keyword matching, requiring constant manual updates. AI CX systems use LLMs with natural language understanding, dynamic response generation, and can handle complex multi-turn conversations. They automatically update as documentation changes and can reason through problems rather than just matching keywords.

What is RAG and why is it important for customer support?

RAG (Retrieval-Augmented Generation) is a framework that grounds LLMs in company data by retrieving relevant content from knowledge bases, augmenting the AI's context with verified information, and generating accurate responses. This reduces hallucinations and enables AI to provide reliable, citation-backed answers about your specific products and policies.

What are MCPs (Model Context Protocols)?

MCPs are an open standard created by Anthropic that allows AI agents to connect with multiple tools and systems without custom integration code. Instead of building one-off integrations for each tool, MCPs create a common protocol that handles the connection between AI agents and enterprise tools, enabling scalable automation.

What's the difference between AI agents and chatbots?

While chatbots primarily answer questions, AI agents can reason, plan, and execute tasks autonomously. They can close tickets, update systems, draft content, and orchestrate across multiple tools. AI agents own the resolution and do the actual work rather than just providing information.

Should we build or buy an AI CX solution?

In 2025, most enterprises are choosing to buy mature third-party platforms rather than build in-house. Building requires significant engineering resources, ongoing maintenance, and expertise in rapidly evolving AI technologies. Buying provides faster time-to-value, proven reliability, and continuous updates without the maintenance burden.

What teams benefit from AI CX beyond customer support?

• Documentation teams: Auto-generate FAQs and update docs based on support resolutions
• Sales teams: Get instant technical answers and automated follow-up drafting
• Product teams: Analyze customer conversations for feature requests and pain points
• Marketing teams: Create content based on actual customer questions and needs

How do we evaluate AI CX platforms?

Ask four key questions:

1. Is it accessible to both developers and non-developers?
2. How does it integrate with existing software?
3. Can you customize agents to be brand-aligned and trustworthy?
4. Does it add extra load on engineering teams?

What ROI can we expect from AI customer experiences?

While specific results vary, properly implemented AI CX typically delivers:

• Significant reduction in tier-1 support tickets
• 24/7 availability without additional headcount
• Faster resolution times (seconds-minutes vs. hours)
• Improved customer satisfaction through instant, accurate answers
• Freed human workforce to handle complex, high-value interactions across all customer facing functions

What's next for AI customer experience?

The future includes more sophisticated multi-agent orchestration, deeper integration with enterprise systems via MCPs, voice and visual AI capabilities, and AI agents that can handle end-to-end customer journeys autonomously. The focus is shifting from answering questions to actually resolving issues.