What is agentic AI? How it’s changing contact center communication

Agentic AI refers to artificial intelligence systems that can operate autonomously toward specific goals without constant human direction. Unlike traditional AI, which typically waits for input before taking action, agentic AI proactively perceives context and takes initiative—whether that means drafting a response, flagging an issue, or surfacing a key insight.

This shift from reactive to proactive intelligence marks a major leap in how AI can function within contact center settings. Beyond just enhancing productivity, agentic AI can begin to act as a co-worker that can handle tasks without being told explicitly what to do.

Agentic AI vs. AI agents

While the two terms are sometimes used interchangeably, there’s a subtle but important distinction between agentic AI and AI agents.

AI agents are similar to advanced chatbots that can perform specific tasks like answering a question or searching a database. They are very useful, and do leverage AI, but usually require inputs or commands to operate. You’ll often come across AI agents on a website’s chat window or when messaging a company on digital channels like WhatsApp.

Agentic AI, on the other hand, refers to an AI system that can operate autonomously without being explicitly directed at each step. In other words, AI agents can be agentic—but not all of them are.

Think of it this way:

• An AI agent might help answer a support ticket.

• An agentic AI system could notice a recurring issue across tickets, create a help doc, notify the right team, and begin routing similar tickets more efficiently without being asked.

How does agentic AI work?

Agentic AI works by following a continuous cycle of perception, reasoning, action, and learning, allowing it to make decisions and execute tasks autonomously. Unlike rule-based systems, agentic AI uses large language models (LLMs) to interpret context, plan intelligently, and adapt over time. This dynamic process enables a level of automation that mirrors human intuition, making agentic AI a powerful tool for modern contact centers.

1. Perceive: Agentic AI begins by perceiving its environment, such as analyzing data from customer interactions, CRM systems, or communication channels. Using natural language understanding and contextual awareness, it identifies key patterns, intents, and opportunities for action without needing explicit instructions.

2. Reason: Once it understands the situation, the AI reasons through possible outcomes using its language model and goal-oriented logic. It evaluates context, priorities, and potential constraints to determine the best course of action, similar to how a human agent would think through a problem before responding.

3. Act: After reasoning, agentic AI takes proactive steps to execute decisions, whether that’s generating a customer response, routing a ticket, or flagging an at-risk account. This ability to act independently is what transforms agentic AI automation into a powerful force for improving efficiency and responsiveness in contact centers.

4. Learn: The system continuously learns from its outcomes, refining its models and improving over time. Each action provides feedback, helping the AI adjust to new situations, customer preferences, and business goals, creating a self-improving loop that keeps performance evolving.

How is agentic AI used in contact centers?

Today, many businesses are deploying Agentic AI to improve customer service by handling complex, multi-step tasks autonomously, including  repetitive and time-sensitive communication tasks. For example, in meetings it’s common to see AI communications platforms like Dialpad automatically transcribing conversations, detecting action items, and summarizing takeaways. 

In customer interactions, contact center AI tools can monitor sentiment in real time and prompt actions to agents based on playbooks. These capabilities are just the beginning, below are some of the most impactful ways agentic AI is being used to transform customer service today:

• Automatically diagnosing a technical issue by referencing a knowledge base and initiating a fix or providing a step-by-step guide.

• Handling refund requests by checking order details, confirming eligibility, and executing the refund process without human involvement.

• Notifying customers about service outages or upcoming expirations.

• Monitoring interactions for quality and compliance, and flagging risky language or high-frustration calls before they need to be escalated.

• Initiating chats if user behavior suggests confusion or frustration (e.g., stuck in a payment flow).

• Negotiating subscription downgrades or cancellations with dynamic offers.

• Resolving billing disputes by retrieving transaction histories, explaining charges, and applying credits if needed.

How to evaluate and implement agentic AI in your contact center

A good agentic AI platform has to be able to perceive context, retain memory across interactions, reason through complex decisions, and take action through APIs or external tools. While it’s possible to build these systems from the ground up, the cost and complexity of doing this can be a significant barrier for most organizations—and not the best investment of time and resources.

In some cases, it’s better to take a more scalable approach, which often begins with an assessment of your current tech stack. Work with your IT leadership to identify:

• Which tools already support agentic capabilities 

• Where integration or scalability may become bottlenecks

Often, choosing a platform with native AI functionality, especially in communication-heavy environments like contact centers, can significantly accelerate time to value and reduce operational overhead.

Here are some examples to show the range of agentic AI companies already in the market today:

• Dialpad - An omnichannel contact center platform with native AI capabilities. Today, it supports real-time agent assistance and intelligent transcription, and is expanding into fully agentic use cases (such as autonomous order tracking, appointment scheduling, and proactive follow-ups) without the need for manual intervention by agents or supervisors.

• AutoGPT and LangChain: Open-source frameworks that allow developers to deploy AI agents capable of handling multi-step tasks.

• ReAct Agent: Built on the ReAct (Reasoning + Acting) framework, this AI agent combines large language model reasoning with real-time decision-making and external tool interaction.

• Zapier, Make, Workato (and other automation platforms): Low-code automation tools that can power agentic AI workflows when paired with LLMs.

• Google, AWS, Microsoft (and other hyperscalers): Cloud providers that supply foundational models and orchestration components to enterprises that prefer to build and customize agentic AI solutions in-house.

• Sierra, Cresta, Parloa and other specialist startups: Companies focusing deeply on niche applications like IT help desks and European telecom—delivering agentic AI with domain-specific precision and speed, often at the expense of general-purpose breadth.

Future trends and what's next

AI in customer support is becoming more than just task-based assistants. Examples include agentic AI applications that can automatically authenticate customers, reschedule appointments, provide order status updates, and more, all without being explicitly told to do so.

Imagine: AI-powered voice and chat agents that can access workflows, APIs, and business systems to get things done on your behalf, completely autonomously. As agentic AI tools become more advanced, they’ll be able to do more than just supporting customer communications—they will help lead them, coordinating across teams and autonomously updating systems of record like CRMs or ticketing platforms.

Hybrid human + AI teams

Agentic AI is not replacing agents; it is empowering them. By taking over repetitive, low-value tasks like data entry or routine follow-ups, it allows human agents to focus on empathy-driven and complex customer interactions. This hybrid model combines human judgment with AI efficiency, resulting in faster resolutions and better customer experiences.

Multi-agent collaboration

The future of contact centers will include multiple AI agents working together seamlessly across channels and workflows. One agent might verify a customer’s identity while another retrieves account data and a third schedules a follow-up. This kind of multi-agent collaboration enables complete, end-to-end issue resolution without manual intervention.

Self-learning and continuous improvement

Agentic AI continuously learns from every interaction, using feedback loops and performance outcomes to refine its decision-making. Over time, this self-learning capability helps the AI become smarter and more adaptive, improving accuracy, speed, and customer satisfaction with each engagement.

Predictive and proactive service

Rather than waiting for customers to reach out, agentic AI anticipates needs and takes initiative. It can detect early signs of frustration, send proactive alerts, or offer assistance before an issue escalates. This predictive approach shifts customer service from reactive problem-solving to proactive engagement.

Governance and responsible AI

As agentic AI systems gain autonomy, strong governance becomes essential. Organizations must ensure that these systems operate transparently, adhere to ethical guidelines, and comply with data privacy regulations. Responsible AI frameworks help maintain trust while still allowing innovation to thrive.