When it comes to customer support, AI is already playing a central role in shaping the experience.
Before an agent ever gets involved, customers often interact with some form of AI–whether it’s answering simple questions, routing requests, or surfacing information. These systems generally fall into two categories: conversational AI and agentic AI.
Conversational AI includes the scripted chatbots we’ve come to expect: helpful for basic tasks, like navigating a website or initiating a service request. Agentic AI, by contrast, consists of more advanced AI agents that can reason, act independently across systems, and continuously improve over time.
Agentic AI refers to systems composed of autonomous agents that can reason, plan, take action, and learn independently or collaboratively across software environments to achieve goals on behalf of humans. These agents are not just assistants. They’re collaborators capable of executing work, making decisions, and learning from every interaction.
That’s why agentic AI isn’t just an upgrade—it’s a fundamental shift.
This shift is being accelerated by powerful large language models (LLMs), which allow agents to handle dynamic, multi-step interactions and deliver more personalized, efficient experiences at scale. While conversational chatbots were built to follow scripts and answer routine questions, agentic AI agents integrate with existing systems through APIs, enabling them to resolve complex issues, trigger actions, and deliver real-time insights. For example, they can automatically trigger a shipping update from a CRM and messaging platform, reducing manual effort while increasing customer trust.
And like any great teammate, they improve with experience. In fact, 42% of companies have already fully integrated AI into their customer interactions, according to a recent RingCentral survey.
To meet rising employee, customer, and investor expectations, leaders must understand how AI agents differ from traditional chatbots—and why deploying secure, integrated agentic AI is now a strategic imperative.
The shift toward smarter, more strategic AI
LLMs are advancing fast, making AI agents better at contextualizing, reasoning, and adapting.
But it’s not just about technical progress. It’s about rethinking how work gets done.
Traditional chatbots operated in silos and struggled with complex inquiries. In contrast, AI agents connect across systems to deliver seamless experiences. For example, they can automatically trigger a shipping update from a CRM and messaging platform, reducing manual effort while increasing customer trust.
From a siloed tool to a strategic team member
While chatbots and AI agents are often conflated, the difference in capability is dramatic.
Chatbots can provide information. AI agents deliver outcomes.
They don’t simply repeat answers—they take action. From resolving support tickets to routing sales leads, AI agents complete tasks that used to require human effort.
These agents can operate autonomously, continuously improve through use, and integrate across systems. That makes them more than just digital assistants—they become trusted team members, helping businesses scale operations, reduce manual workloads, and drive better customer results.
Here’s what makes AI agents different, and why it matters:
- Task completion vs. information delivery: Chatbots were designed to repeat information to users. AI agents are designed to respond to dynamic prompts and execute complete workflows to support broader business goals, ultimately helping to deliver more significant results.
- Decision-making autonomy: AI agents don’t follow rigid scripts—they assess real-time conditions and choose the best course of action. That means fewer bottlenecks, faster resolutions, and better outcomes for customers.
- Integration capabilities: Many chatbots could only generate responses based on information in a single system. With APIs connecting AI agents to multiple systems, like CRMs, CX platforms, or ERPs, they can deliver more unified intelligence and automate workflows.
- Learning and adaptation: To introduce new capabilities or fix issues in a legacy chatbot, organizations had to update to a newer version manually. But AI agents learn through interactions and become more intelligent and adept at specific tasks the more they’re used.
- Unified compliance and easy auditability: Messy, inconsistent data can make compliance harder and audits difficult. But when the underlying platform supporting AI agents is connected to other third-party tools, companies can create more consistency in their record-keeping to improve customer service and legal compliance.
A transformation, not a transition
The transition from chatbots to AI agents is a shift from scripted, reactive tools to autonomous systems that can reason, remember, and act across platforms.
This isn’t just a tech upgrade. It is a redefinition of how work gets done across the enterprise.
AI agents fundamentally change how customer experience teams work, and can deliver results at a speed and scale that far surpasses the impact of chatbots. Reductions in operational costs and improvements in customer loyalty contribute directly to broader business goals, like greater efficiency or sales growth, helping service centers finally shed their “cost center” reputation.
Some of the most common outcomes from AI agents include:
- Customer service transformation: The jump from chatbots answering questions to AI agents resolving issues can eliminate friction in customer journeys.
- Operational efficiency gains: By automating both routine and complex workflows, AI agents reduce manual intervention in service and support requests, enhancing the speed and quality of support. And features like real-time sentiment analysis can save managers hours of work every week.
- Employee productivity enhancements: For calls that ultimately require human intervention, AI agents support specialists with real-time intelligence, streamlining previously communication-heavy tasks.
And in more advanced deployments, that doesn’t mean just one agent.
Multiple AI agents can collaborate across workflows—each focused on a specific task, but working together to resolve issues faster. One agent might handle identity verification, while another simultaneously pulls up order history or initiates a cancellation.
This kind of ecosystem intelligence streamlines resolution in seconds and allows organizations to build a truly scalable AI-powered workforce.
RingCentral’s approach to agentic AI
At RingCentral, we’re focused on helping customers deploy AI agents that work with specialists, not just alongside them, to improve every interaction. From automating calls to delivering real-time coaching or post-call analysis, our AI solutions are built to amplify human potential and ensure every customer experience is seamless, personalized, and efficient.
Take RingCentral AI Receptionist, for example. It can handle inbound calls autonomously, ensuring no inquiry is missed, even after hours. Meanwhile, our AI-powered contact center tools provide agents with live sentiment analysis and contextual recommendations, enabling them to navigate even the most complex conversations with greater confidence and speed.
Importantly, these tools aren’t bolted on. They’re embedded within an organization’s existing systems, connected to the tools your teams already use—CRM, messaging platforms, support workflows—to reduce silos and improve continuity.
One of RingCentral’s key differentiators? Voice.
Voice is the most information-rich signal in business communication.
By analyzing tone, urgency, and intent, not just words, our voice-first approach helps AI agents become more emotionally aware and responsive. If a caller’s tone shifts toward frustration, the agent can offer to escalate to a live rep. If urgency is detected, it can prioritize the issue or suggest faster resolution paths—all in the moment.
That’s the context that text-based agents often miss. Without vocal signals like inflection, pacing, or emotion, chatbots may overlook cues indicating when a conversation is going sideways or trust is at risk.
By anchoring agentic AI in voice, RingCentral delivers a more human, more trustworthy form of automation that builds connections, not just efficiency.
How to get started
Once business leaders understand how much more capable and powerful AI agents are compared to chatbots, the immediate question is often: how do we get started?
It’s critical that organizations take the time to implement this technology safely and securely. Many companies aren’t ready to build their own AI systems from scratch. That’s why offerings within existing, comprehensive platforms are so game-changing. It makes it easy for organizations to introduce the technology into existing call center operations, without complicating IT landscapes by bringing in another vendor or solution.
Beyond implementation, employee training is key. With the proper change management, companies can close knowledge gaps, uncover early edge cases, and help teams start generating value faster.
But training is just the beginning. Agentic AI is coming, and here’s how to prepare your teams now:
- Start with pilot use cases: Identify repeatable, time-consuming tasks where AI agents could alleviate pressure and free up human capacity.
- Get your data house in order: Ensure your data is high quality, well-structured, and integrated across systems. AI can’t act effectively without clean context.
- Establish governance guidelines: Create internal policies for AI use, especially around decision-making, explainability, and transparency.
- Invest in team readiness: Provide training that goes beyond the interface. Help employees understand how to work with AI agents, interpret insights, and provide oversight.
For a deeper look at our approach to agentic AI, read the full blog here: https://www.ringcentral.com/us/en/blog/the-voice-first-approach-to-agentic-ai/.
Originally published Jul 02, 2025
