Is this the end of the chatbot era as we know it? The rise of agentic AI in customer service marks a shift toward autonomous, decision-capable agents at the point of experience, one that’s changing self-service from “find the answer yourself” to “get it done on your own without waiting.”
If nothing else, the role of transactional chatbots is narrowing, while the gap between traditional support models and what customers now expect is widening. The differences are anything but subtle:
Global 2025 research from Cisco found that business leaders expect 68% of customer experience interactions to be handled by agentic AI within three years. Gartner analysts agree: they predict agentic AI will autonomously resolve 80% of common service issues by 2029.
What exactly makes agentic AI different from the chatbots and GenAI tools already in place? More importantly, what does it take to make it work at the enterprise level?
What Is Agentic AI in Customer Service?
Chatbots and LLM summarizers can retrieve information and generate responses, but they stop at the point of action. Users are returned to a form, queue, or another service channel to get the job done.
Agentic AI closes that gap by resolving issues in real time (task automation), such as cancelling a subscription, updating billing information, or rebooking a shipment. It does so through context-aware decision-making, in which AI interprets the full picture of the request, reasons through required steps, and executes across connected systems.
In short, agentic AI acts on behalf of the user. Implemented properly, it can autonomously understand customer data and intent, reason through the required steps, and execute tasks end-to-end.
Examples of Agentic AI in Customer Service
For customer service leaders tracking resolution times, escalation rates, and cost per contact, agentic AI changes the math on scenarios like these:
- A customer disputes a charge. Without routing to a specialist, the AI agent investigates the transaction, cross-references the account history, validates the claim, and issues a credit.
- A subscriber wants to upgrade their plan. During one interaction, the AI agent calculates the prorated difference, applies it, confirms the new features are active, and sends a summary.
- A patient needs to reschedule a procedure. The AI agent checks provider availability, verifies insurance authorization, updates the appointment, and triggers the pre-visit instructions—no transfers or callbacks.
Replacing human intervention like this isn’t hypothetical.
In January 2025, OpenAI launched Operator, an agentic AI tool that can browse the web, click buttons, fill out forms, and complete purchases on behalf of users. During a live demo, a user uploaded a photo of a handwritten grocery list. Operator built the order on Instacart and scheduled delivery without further input. The tool now lives inside ChatGPT as “agent mode”.
Illumio’s Real-World Agentic AI Implementation
Illumio recently combined Salesforce Agentforce with Coveo’s AI-relevance layer, which enabled:
- Retrieval tailored by granular context (agent role, account tier, etc.)
- Autonomous customer inquiry resolution and knowledge generation
- Faster self-service success without sacrificing accuracy
And yes, there were skeptics. Internal testers went from lukewarm feedback to what VP of Support Operations Joyce Leung described as “literally saying ‘wow.'” (Watch the full story.)
Do Agentic AI and GenAI Work Together? Yes.
It’s easy to assume that agentic AI is replacing GenAI in customer service. The truth is that best enterprises use them together to create a complete customer support experience.
That said, the partnership only works when both are grounded in the same enterprise content. After all, fragmented AI deployments are simply unsustainable.
Relevant Reading: Agentic AI vs Generative AI: What Is the Difference?
The Building Blocks of Agentic AI in Support
Four high-level AI capabilities give agentic AI its autonomous end-to-end ability:
Retrieval-powered intelligence (RAG)
LLMs don’t inherently know your return policy or product specs. Basic vector databases aren’t enough to fix that. You need what we call relevance-augmented retrieval. This form of advanced hybrid retrieval combined with AI ranking can:
- Interpret user intent
- Dynamically connect to the most relevant info
Studies show RAG can increase base model accuracy by 40%. Even when accuracy gains are modest, the modularity enables more refined auditing and trust.
Contextual grounding and permission-aware security
Effective function requires grounding the agentic AI system in enterprise-approved content, ensuring that every action aligns with organizational policies and accurate information.A strong agentic AI system ranks by relevance to the specific customer—their case history, product line, and intent (see: Coveo Passage Retrieval API). Something like RAG-as-a-Service can help ensure permission-aware responses, only surfacing content that users are authorised to see.
“While LLMs have become widely available, their enterprise value depends on relevance—how effectively they can ground responses in factual, secure, and permission-aware data.”
–Sebastien Paquet, Coveo’s VP of AI Strategy
Business-aligned logic and guardrails
Autonomy without guardrails is a potential liability. Production-ready agentic AI must operate within confidence thresholds. These ensure the system only acts when certainty is high; otherwise, it will escalate to a human agent.
Agentic systems need guidance as to when they should act autonomously, escalate cases, etc. Our customers have found success embedding agentic AI actions within the guardrails of approved enterprise content.
Yes, AI agents can resolve cases, auto-draft emails, and generate knowledge articles, but only when grounded in verified sources.
Modular APIs for continuous improvement
Customer support leaders need control without constantly having to submit engineering tickets. For that, developers need the kind of flexibility provided by our expanded API toolkit, for example. The toolkit includes APIs for search, passage retrieval, case classification, and answers.
They can build AI-powered experiences that are precise, secure, and business-aware. The Case Classification API, for example, helps route unresolved cases to the right team or expert automatically.
What’s Driving the Shift to Agentic AI in Customer Service?
- The push to do more with less right now. At the same time that customer support teams are being asked to cut costs, they’re expected to handle more volume. Seventy-seven percent of agents report higher and more complex workloads than a year ago, while 56% say they’re experiencing burnout. AThe economics don’t hold without a different approach.
- Deflection has hit a ceiling. Seventy-two percent of customers will abandon a brand’s website entirely after a negative self-service experience—heading straight to Google or a competitor. They’re not escalating to agents. They’re just gone. The top complaint? Over half say they simply can’t find the information they need on their own.
- Content discoverability remains a problem. Most organizations have the knowledge to resolve common issues. It’s scattered across systems, buried in PDFs, or structured in ways that don’t surface when customers need it. Only 22% of organizations use unified search to bring content silos together; just 19% use machine learning to surface the most relevant results.
- Customer needs have changed. Coveo’s 2025 CX Relevance Report found that preference for live agent access dropped from 64% to 56% year-over-year, while demand for AI-guided assistance within products jumped 7 points. The generational split is stark: Gen Z and Millennials use an average of four channels to get support, expecting seamless self-service across all of them. Baby Boomers still default to the phone.
What This Means for Your AI Roadmap
Gartner analysts warn that C-level leaders have just three to six months to define their agentic AI strategies—or risk being outpaced by faster-moving competitors. predicts that 40% of enterprise applications will include task-specific AI agents by the end of 2026—up from less than 5% today. By 2027, 86% of companies expect to be operational with AI agents. That’s not a forecast for early adopters.
Meanwhile, Forrester’s 2026 predictions offer a sobering counterpoint: next year won’t be the year AI transforms customer service. It will be the year of foundational work:
- Simplifying tech stacks
- Consolidating vendors
- Fixing the data quality issues
Enterprises that invest now in retrieval infrastructure, content unification, and grounded AI will be positioned to scale when agentic capabilities mature. Those who wait may spend the next two years catching up.
Customer expectations aren’t waiting either. A 2025 Salesforce survey found that 82% of service reps say customer expectations are higher than ever
You don’t have to go all-in on day one. The smartest enterprises are doing strategic experiments with AI. They’re deploying generative AI in controlled deployments to prove value, build retrieval infrastructure, and generate the cost savings that fund their agentic pilots.
Dig Deeper
See what happens to efficiency and customer satisfaction when generative AI is actually grounded in your enterprise content: Watch the demo