Customer Experience 
Agentic AI is the next frontier in AI development, after generative AI. This phase of artificial intelligence involves proactive systems that can understand, make decisions, and act with minimal oversight. An agentic AI system provides a framework where multiple autonomous AI agents coordinate to achieve specific user goals.
Where generative AI creates responses, agentic AI goes beyond merely answering and performs complex actions necessary to reach expected user outcomes. It takes actions without much human intervention. The key difference between agentic AI and generative AI is that agentic AI focuses on decision-making, while generative AI focuses on creation. Unlike traditional AI, agentic AI is more autonomous and proactive, capable of managing complex tasks without constant user input.
AI agents can delegate critical actions to humans. However, agentic AI automates the majority of decisions and operations before reaching critical actions. Most importantly, it does everything proactively without waiting for users to give a prompt.
It makes the agent and customer experience truly connected, moving them closer to achieving business objectives.
Where Do You Rank on the AI Maturity Curve?
Take the 5-minute assessment and get a personalized score and recommended next steps.
What Is Agentic AI?
Agentic AI is an advanced level of artificial intelligence automation that adapts to new data and learns on its own. It operates autonomously and executes tasks based on real-world data. The requirement for human intervention is minimal. Agentic AI plans, reasons, and adapts to optimize complex workflows for expected outcomes. It is built using tools like large language models, machine learning, and natural language processing to perform tasks on behalf of users.
These autonomous agents can perceive their environment and make decisions with minimal human guidance. Agentic AI is part of a broader class of autonomous systems capable of independent decision-making. Traditional AI systems only act when prompted, but agentic AI plans and executes actions to achieve objectives, rather than simply generating output and stopping. Agentic AI systems can analyze and process data as core capabilities, enabling them to interpret large datasets and develop strategies to perform tasks independently.
Overall, agentic AI doesn’t require a human prompt at every step to achieve its objectives. It takes initiative, plans steps, and progresses toward the objective. Additionally, it can automate internal workflows to make it easier on human employees without their physical intervention. Reinforcement learning is a key technique used by agentic AI to improve its actions through feedback, enabling it to learn and adapt over time.
Key Characteristics of Agentic AI Systems
Agentic AI systems are built from foundational building blocks that enable scalable and modular automation across enterprises. These systems often include specialized agents, each tailored to handle specific organizational functions or tasks, streamlining workflows and improving operational efficiency.
Agentic AI systems are designed to autonomously manage multi-step processes and complex tasks independently, making them highly effective for intricate, real-world business operations. Below are some of the key characteristics of agentic AI systems.
Driven by objective
Agentic AI systems require clearly defined goals, which they can break down into sub-tasks and plan action sequences. You provide the system, or it formulates specific objectives and works toward achieving them.
For example, you assign an agent the goal of scheduling a meeting. Agentic AI will find a time slot, invite attendees, book a meeting room, and alert all attendees a short time before the meeting begins. This is how it carries out multistep tasks.
Autonomous
These agents don’t require human approval at every step to accomplish an objective, whether preset by a human or formulated independently by the agent. Agentic AI systems are designed to operate with minimal human intervention, enabling them to assess situations, solve problems, and interact with their environment autonomously. However, this autonomy depends on how these agents are configured and can vary.
Some agents may request confirmation before taking critical steps, while others reach their goal without virtual supervision. For example, a trading agent can execute, sell, or buy orders on the financial market without a person controlling it.
Interactive
Agentic AI interacts with the physical or virtual world and takes actions to affect it. Simply put, the agent responds to environmental changes and adapts accordingly. This interactivity allows these agents to operate in dynamic and unpredictable settings, enabling them to handle complex scenarios by perceiving, reasoning, and acting to achieve specific goals.
Adaptive
Agentic AI incorporates mechanisms to learn from experience. The more feedback you give, the better it adapts to new situations and environments. AI agents learn by continuously interacting with and retrieving diverse data sources, which creates a feedback loop that refines their models and enhances performance over time.
For example, a virtual assistant learns a user’s unique preferences after a few interactions, hyper-personalizing the user’s experience with the agent.
These agents aren’t stuck in a single hard-coded sequence, making them more flexible in handling unexpected events.
Persistent
Agentic AI operates over extended durations through multiple steps to achieve goals. This means that the agent can combine state and context over time, enabling users to achieve long-term objectives with minimal human intervention.
These agents can complete tasks that require sustained effort and coordination, such as executing workflows or orchestrating processes across enterprise systems. These agents are proactive rather than reactive. They take initiative while deciding what needs to be done and when. For example, an agent working on your email might draft a response for an urgent email.
This makes agentic AI not just any other tool in a user’s hand but a tool with a degree of self-directed agency.
How Does Agentic AI Work?
Agentic AI solves complex problems through a five-step process:
- Set a goal: Typically, a human assigns agentic AI a set of goals to achieve. Sometimes, the goal of agentic AI is derived from a higher objective. The goal can be as simple as “manage email inbox effectively.”
- Perceive: Agentic AI systems gather data from various sources, including real-time data and system logs, to understand their environment. It extracts meaningful features and recognizes objects while identifying relevant environmental entities. This gives the agent context and situational awareness.
- Reason: Advanced AI models orchestrate reasoning, helping agentic AI understand specific tasks and coordinate specialized models for different visual processing. Agentic AI uses external tools and data analysis to interpret information and solve problems efficiently. The agent uses retrieval-augmented generation (RAG) to access proprietary data sources and deliver accurate results. Agentic AI might also leverage a knowledge base for structured or unstructured data, neural networks for classification and prediction, and Belief-Desire-Intention (BDI) models to track its perception of the world.
- Act: After planning, agentic AI performs physical or digital actions in its environment. This action moves the agent closer to the goal. The physical actions include activities like driving or navigation. On the other hand, digital action involves sending an email, modifying database records, or making API calls.
- Learn: Agentic AI learns through feedback loops, refining and improving its performance over time. It can adapt and become more effective with feedback, helping businesses achieve better outcomes as it matures. It learns from reward signals or penalties and updates the model as new datasets enter the system. When real-world data is limited, synthetic data can be utilized to enhance learning and improve model performance.
Agentic AI in Sales and Customer Service
Businesses are increasingly implementing agentic AI to personalize customer service and streamline operations. Agentic AI is being adopted to optimize business processes and address evolving enterprise needs, enabling organizations to automate complex workflows and adapt to changing requirements.
Below are some use cases of agentic AI on the sales side.
Sales use cases of agentic AI
Agentic AI can play a phenomenal role in sales, especially in lead qualification, sales outreach, dynamic pricing, and pipeline forecasting.
By integrating real-time market research, agentic AI can inform sales strategies and product development, ensuring offerings are aligned with consumer needs.
Additionally, agentic AI is capable of addressing complex challenges in sales operations, such as analyzing large datasets and orchestrating multi-step workflows. It can also contribute to boosting developer productivity by automating repetitive sales-related coding tasks, allowing teams to focus on more strategic initiatives.
Automated lead qualification
Agentic AI ingests vast amounts of data from customer relationship management (CRM) software to get the context of a prospect’s behavior on a website. This connection with a CRM fuels the agent’s perception mechanism. It assesses the prospect’s interaction on the webpages and monitors their social interactions. By analyzing data, agentic AI can recognize patterns in prospect behavior to qualify leads, identifying whether a prospect is of high purchase intent. The agents continuously improve as they assess patterns and analyze how many such interactions convert into sales.
This makes it easier for the agent to prioritize high-intent prospects for human sales agents. Live contact center agents can reach out to them methodically to build trust and take them toward conversion.
AI-powered sales outreach
With behavioral data, agentic AI identifies triggers where a potential buyer needs nudges to move toward the agent’s objective, i.e., a sale or upsell. After identifying the trigger, the agent can draft emails or send relevant notifications to the buyer to automate follow-ups.
These notifications are automated follow-ups or trigger nurturing sequences to move the prospect toward signing a deal.
Dynamic pricing and upselling
Dynamic pricing involves making real-time adjustments to prices based on market demand and competition. Agentic AI continuously scans competitors’ pricing, promotions, and availability to provide quotes that ensure profits while remaining competitive.
These adjustments are implemented instantaneously based on external signals and market fluctuations. Agentic AI is capable of handling complex scenarios involving fluctuating market conditions and diverse customer behaviors, enabling it to make sophisticated pricing decisions even in dynamic and unpredictable environments.
This doesn’t mean that prices are reduced beyond the lowest feasible limit. If conditions are set, agentic AI won’t lower them below the set price. Even when it lowers the cost, it does so in a strategic manner. When competitors run out of stock, prices can be increased to recover any discounts previously offered.
Agentic AI also considers customer interactions and purchase patterns to determine the optimal price at which deals are likely to be accepted. This maximizes conversions to win over customers.
This is similar to the way travel booking platforms adjust pricing for customers based on their past bookings. The agent evaluates contextual data, such as holidays or time of day, to set prices. It adjusts them proactively to match anticipated demand fluctuations.
Similarly, the agent autonomously identifies upsell opportunities personalized for customers.
Pipeline forecasting and insights
Agentic AI analyzes historical data to study sales trends and identify bottlenecks in the process. Based on its analysis, it modifies workflows to guide users toward deal closures or sends recommendations to sales agents.
Agentic AI is connected to CRM, customer data platforms, and other business systems that consistently monitor customer behavior and agent activity. It uses data from these systems to run its analysis, delivering real-time insights that help sales representatives gain a clear picture of their pipeline. These insights enable realistic forecasting of the sales pipeline, keeping predicted numbers close to the actual results.
Customer service use cases of agentic AI
On the customer service front, agentic AI serves customers and the company in various ways. Increasingly, agentic AI is being used to efficiently handle customer service inquiries, improving response times and enhancing the overall customer experience.
Automated issue resolution
Agentic AI autonomously manages customer queries and guides them toward resolution across chat, voice, and email channels. This increases the first-contact resolution rates at call centers while ensuring customers have a satisfactory service experience. Emerging trends indicate that agentic AI will play a crucial role in enhancing customer engagement and satisfaction.
These agents are available 24/7 to provide instantaneous support, ensuring customer satisfaction whenever they reach out.
Nvidia states, “An AI agent for customer service, for instance, could operate beyond simple question-answering. Agentic systems could check a user’s outstanding balance and recommend which accounts could pay it off — all while waiting for the user to decide so it could complete the transaction accordingly when prompted.”
Proactive customer support
Agentic AI predicts potential service disruptions and alerts customers before issues escalate. The agents take the initiative to prevent dissatisfaction or churn. They are equipped with predictive analytics capabilities to identify patterns signaling potential customer issues.
Based on the actual usage data, these agents suggest personalized upgrades or alternative actions to avoid the issue in the future.
AI-guided human agent
Agents receive real-time insights when they’re in a conversation with a customer. This ensures the agent has everything they need to offer a quick resolution to the customer, drastically improving resolution speeds.
With agentic AI insights, customer service representatives can immediately suggest the next course of action. After successful interactions, the agent learns and improves its recommendations over time.
Omnichannel AI assistants
Top-rated contact center software, such as Nextiva, has support systems integrated across email, social media, chat, and phone. They maintain a unified customer support channel to give buyers a consistent experience.
Agentic AI can integrate with these systems through AI and deliver autonomous support across all communication channels. It retains context from previous interactions and provides a seamless experience for the customer, thereby eliminating the friction found with legacy support tools.
What You Need to Implement Agentic AI
Before implementing agentic AI, clearly define its use cases and the measurable business goals that you aspire to achieve with the technology. Guiding the path forward is essential, as it steers adaptive problem-solving and decision-making processes to ensure the successful implementation of agentic AI.
Only then should you move toward setting up the requirements. A comprehensive assessment ensures that agentic AI aligns with the overall business strategy and minimizes disruptions.
Data infrastructure and integration
Agentic AI needs robust data collection, storage, and processing systems for real-time analysis and decision-making. Ensure your data warehouses or lakes give easy access for consolidating and cleaning existing data. Incorporating diverse data sources, including patient data in healthcare applications, is crucial for enhancing the performance of agentic AI, as it enables more accurate, adaptable, and privacy-conscious outcomes.
Integrate CRM software, ticketing systems, an enterprise resource planning (ERP) platform, and an analytics platform to support the AI agent in making informed decisions.
Workflow automation and APIs
Ensure that the tools in your tech stack support process automation triggered by artificial intelligence. AI-powered agents will deliver AI capabilities to perform various tasks across multiple platforms, enabling them to achieve their business objectives.
Implementing agentic AI can reduce operational costs by automating tasks traditionally handled by manpower. Key benefits include automating routine tasks, streamlining administrative tasks, and enabling AI agents to execute tasks autonomously across platforms.
Ensure that API-first platforms enable AI agents to trigger actions across third-party applications.
AI governance and compliance
AI can pose challenges in terms of compliance. Ensure you set strict guardrails to allow AI to follow GDPR, CCPA, and PCI-DSS compliance regulations. It’s best to have human oversight when making critical decisions.
On the cybersecurity front, relevant controls should be set up to avoid adversarial attacks and data breaches..
Continuous learning and optimization
Systems require ongoing monitoring, tuning, and adaptation to maintain effectiveness. Collect customer and employee feedback as a regular practice to continuously improve AI algorithms. Agentic AI is an evolving technology requiring a culture of continuous learning.
By leveraging agentic AI, organizations can streamline software development by automating and optimizing software engineering processes, resulting in more efficient and adaptable workflows. As interactions increase, the model will automatically learn from new data and improve.
In the contact center industry, AI agents are becoming increasingly standard in customer support. These agents cater to different responsibilities, including:
Examples of AI Agents in Contact Centers
- First-contact resolution: When a customer asks a billing-related question, the agent understands the issue and reviews the customer’s account balance to suggest the best possible payment options. The agent goes beyond to offer discounts based on customer history.
- AI-powered retention agent: It identifies signals that indicate whether a customer is about to churn. Then, it proactively contacts the customer to offer loyalty perks and special discounts.
- AI sales assistants for call centers: These AI agents listen to calls to detect objections and offer real-time suggestions. They automate administrative workloads and repetitive tasks, such as summarizing calls and updating the CRM, making agents more effective in actual sales.
- Multi-channel AI virtual assistant: AI agents enable buyers to use any channel, such as chat, email, or voice, to ask frequently asked questions. The agent goes beyond to schedule appointments and process any related refunds.
Nextiva Is Leading the Future of Contact Center AI
Nextiva’s innovative contact center solution is well-positioned to integrate agent-based AI with business systems. They unify channels like chat, phone, and email to deliver seamless customer support, regardless of the communication channel. The integration with CRM provides the insights necessary to analyze buyer behavior. This supplies ample data to agentic AI, which is required to ensure accuracy.
Nextiva comes with intelligent automation, which is handy for increasing agent productivity and efficiency. When all of these are bundled together, it provides a complete package for integrating agentic AI with its contact center solution.
Try the #1 Rated Contact Center Software: Nextiva
Manage every interaction with the top-rated enterprise contact center to simplify agent and supervisor workflows to elevate the customer experience.
Agentic AI FAQs
Below are a few questions that raise readers’ curiosity about agentic AI.
Generative AI primarily generates content, such as text, images, or code, by learning from existing data. It responds to user prompts and utilizes patterns to craft responses. For example, ChatGPT writes answers when you ask questions, but it doesn’t act on its own.
Agentic AI, however, can make decisions and take actions without waiting for a prompt. It plans, senses its environment, and adjusts based on feedback. Think of it like an intelligent assistant that can complete tasks for you, such as booking meetings or managing workflows, all by itself.
Agentic AI works well in areas where tasks are complex and need decision-making. It’s great for customer service, as it can handle questions and solve problems without requiring human assistance. It’s also useful in sales, marketing, and business operations to automate routine work.
In software development, agentic AI can write and test code, enabling developers to complete projects more efficiently.
The key is having clear goals and good data. If you want agentic AI to help, ensure your tasks are well-defined and that you have reliable information for it to learn from.
Agentic AI can sometimes make wrong or biased decisions if it learns from poor or unfair data. This can lead to unfair treatment or mistakes in essential tasks.
Additionally, because agentic AI operates independently, it can be challenging to understand why it made certain decisions. This “black box” effect makes fixing problems tricky.
To mitigate risks, it’s essential to monitor AI actions and maintain human involvement in critical decisions. Regular checks and updates help keep agentic AI safe and reliable.
