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What is Agentic AI?

The landscape of artificial intelligence is rapidly evolving, and at the forefront of this evolution is agentic AI. As noted by UiPath, “the convergence of powerful LLMs (large language models), sophisticated machine learning, and seamless enterprise integration has enabled the rise of agentic AI, which is the ‘brainpower’ behind AI agents.” This powerful technology represents a significant leap forward in how AI systems can autonomously operate, make decisions, and execute complex tasks.

While traditional AI and generative AI have made significant strides in automation and content creation, agentic AI addresses the crucial gaps in autonomous decision-making and task execution. It’s becoming increasingly clear that this technology will reshape how businesses operate, particularly in areas requiring sophisticated problem-solving and adaptability.

What is agentic AI?

Agentic AI refers to artificial intelligence systems that can autonomously execute tasks, make decisions, and adapt to real-time changing conditions. Unlike more passive AI systems, agentic AI demonstrates agency—the ability to act independently and make choices based on understanding the environment and objectives.

As a side note here: I led a webinar recently called From Contact Center to Agentic AI Leader: Embracing AI to Upgrade CX. My colleague Quiq VP of EMEA Chris Humphris and I went deep into agentic AI specifically for the contact center. I highly recommend you watch the replay or read the recap if you’re interested in how this technology works within the confines of the contact center, and what’s needed to make it successful at the platform level. Here’s a hint:

Agentic AI Platform Requirements

Watch the full webinar here.

How does agentic AI work?

Agentic AI operates through a sophisticated combination of technologies and approaches. As IBM explains, “Agentic AI systems provide the best of both worlds: using LLMs to handle tasks that benefit from the flexibility and dynamic responses while combining these AI capabilities with traditional programming for strict rules, logic, and performance. This hybrid approach enables the AI to be both intuitive and precise.”

The system works by integrating multiple components:

  • Language understanding: Processing and comprehending natural language inputs
  • Decision making: Analyzing situations and determining appropriate actions
  • Task execution: Utilizing APIs, IoT devices, and external systems to perform actions
  • Learning and adaptation: Improving performance based on outcomes and feedback

For example, in customer service, an agentic AI system can:

  1. Understand a customer’s inquiry about a missing delivery
  2. Access order tracking systems to verify shipping status
  3. Identify delivery issues and initiate appropriate actions
  4. Communicate updates to the customer
  5. Automatically schedule redelivery if necessary

This customer service example demonstrates several key advancements over previous generations of AI assistants:

While traditional chatbots could only follow rigid, pre-programmed decision trees and provide templated responses, agentic AI shows true operational autonomy by orchestrating multiple systems and making contextual decisions.

The ability to seamlessly move between understanding natural language queries, accessing real-time shipping databases, evaluating delivery problems, and initiating concrete actions like rescheduling represents a quantum leap in capability.

Last-gen AI would typically need human handoffs at multiple points in this process – for instance, when moving from customer communication to backend systems access or when making judgment calls about appropriate remedial actions.

The agentic system’s ability to maintain context throughout the interaction while independently executing complex tasks showcases how modern AI can function as an independent problem-solver rather than just a conversational interface. This level of end-to-end automation and response was impossible with earlier generations of AI technology.

What is the difference between agentic AI and generative AI?

While both agentic AI and generative AI represent significant advances in artificial intelligence, they serve distinctly different purposes. Generative AI excels at creating content—text, images, code, or other media—based on patterns learned from training data. Agentic AI, however, goes beyond generation to actively make decisions and execute tasks.

Agentic AI vs. Generative AI

These technologies can work together synergistically, with generative AI providing content creation capabilities within an agentic AI’s broader decision-making framework.

Benefits of agentic AI

Key benefits include:

1. Autonomous operation

By eliminating the constraints of human-dependent processes, agentic AI creates a new paradigm of continuous, reliable service delivery that scales effortlessly with business demands. The result is:

  • Reduced human intervention: AI agents handle complex tasks independently, freeing human workers to focus on high-value activities requiring emotional intelligence and strategic thinking.
  • Consistent performance: The system maintains uniform quality standards regardless of workload, time of day, or complexity of tasks, eliminating human variability and fatigue-related errors.
  • 24/7 availability: Unlike human operators, AI agents operate continuously without fatigue, ensuring consistent service availability across all time zones.

2. Improved human-AI agent collaboration

Agentic AI changes the relationship between human agents and technology, creating a symbiotic partnership that enhances overall service delivery and job satisfaction. Here’s how.

  • Ensures consistency: AI agents establish and maintain standard operating procedures across teams, ensuring every customer interaction meets quality benchmarks regardless of which human agent is involved. This standardization helps eliminate variations in service quality, while still allowing for personal touch where needed.
  • Accelerates learning: New agents benefit from AI-powered guidance that provides suggestions and best practices, significantly reducing the time needed to achieve proficiency. The system learns from top performers and shares these insights across the entire team.
  • Reduces training time: By providing contextual assistance, agentic AI helps new agents become productive more quickly. Training modules adapt to individual learning patterns, focusing on areas where each agent needs the most support.
  • Improves agent performance with insights: The system continuously analyzes agent interactions, providing actionable feedback and performance metrics that help identify areas for improvement. These insights enable targeted coaching and development opportunities.
  • Improves job satisfaction and reduces agent turnover: By handling routine tasks and providing intelligent support, agentic AI allows agents to focus on more engaging, complex work that requires human empathy and problem-solving skills. This role enhancement leads to higher job satisfaction and lower turnover rates.

3. Enhanced efficiency

Through intelligent automation and rapid processing capabilities, agentic AI significantly improves operational performance across organizations, resulting in:

  • Faster task completion: AI agents process and execute tasks at machine speed, dramatically reducing resolution times compared to manual processes.
  • Reduced error rates: Systematic processing and built-in validation reduce mistakes common in human-operated systems.
  • Streamlined workflows: Intelligent routing and automated handoffs eliminate bottlenecks and optimize process flows.

4.  Real-time adaptability

The system’s ability to learn and adjust in real time ensures optimal performance in dynamic business environments. It shows this via:

  • Dynamic response to changing conditions: AI agents automatically adjust their approach based on current conditions and new information.
  • Continuous learning and improvement: The system learns from each interaction, continuously refining its responses and decision-making processes.
  • Personalized solutions: Advanced analytics enable tailored responses that account for individual user preferences and historical interactions.

5. Integration capabilities

Agentic AI integrates with existing business systems to create a unified operational environment. Main ways include:

  • More seamless connection: The technology easily integrates with current business tools and platforms, maximizing existing investments.
  • Unified data utilization: AI agents can access and analyze data from multiple sources to make informed decisions.
  • Comprehensive solution delivery: The system coordinates across different platforms and departments to deliver complete solutions.

6. Cost-effectiveness

Implementation of agentic AI leads to significant cost savings and improved resource utilization. Top areas for savings include:

  • Reduced operational costs: Automation of routine tasks and improved efficiency lead to lower operational expenses.
  • Intelligent workload distribution: Ensures optimal use of both human and technological resources.

Use cases for agentic AI

Agentic AI’s applications span numerous industries and use cases. Let’s look at the top four industries that are ripest for benefits from our perspective, and the use cases that are best poised for AI.

1. Customer service

In customer service, agentic AI improves support operations from reactive to proactive, enabling intelligent interactions that enhance customer satisfaction while reducing costs. Top use cases include:

  • Query resolution.
  • Ticket management
  • Proactive support
  • Personalized assistance

2. eCommerce and retail

In retail and eCommerce, agentic AI revolutionizes the retail experience by creating seamless, personalized shopping journeys while optimizing backend operations for maximum efficiency and profitability. Best use cases include:

  • Inventory management
  • Personalized shopping recommendations
  • Order processing
  • Customer engagement

3. Business automation

By integrating intelligent decision-making with execution capabilities, agentic AI streamlines complex business processes and eliminates operational bottlenecks across organizations. Start automation targeting:

  • Supply chain optimization
  • Process automation
  • Resource allocation

4. Healthcare

Agentic AI enhances patient care and operational efficiency by combining real-time monitoring with intelligent decision support and automated administrative processes. From what we’re seeing, the biggest opportunities to apply agentic AI rest in:

  • Patient monitoring
  • Treatment planning
  • Diagnostic support
  • Administrative tasks

Agentic AI challenges

Let’s take a look at the biggest challenges with agentic AI right now.

1. Ethical considerations

The autonomous nature of agentic AI raises ethical concerns that require careful attention. These systems, designed to make independent decisions and take action, must operate within established ethical frameworks to ensure responsible deployment.

Key ethical challenges include:

  • Accountability for AI decisions and actions
  • Transparency in decision-making processes
  • Potential bias
  • Impact on human autonomy and agency

Quiq SVP of Engineering Bill O’Neill recently talked to VUX World’s Kane Simms about this very issue:

2. Data security

Data security represents a critical challenge in agentic AI implementation, as these systems often require access to sensitive information to function effectively. (If you’re curious, you can learn about our approach to security here).

Primary security concerns include:

  • Protection of training data and model parameters
  • Secure communication channels for AI agents
  • Prevention of adversarial attacks
  • Data privacy compliance (GDPR, CCPA, etc.)
  • Access control and authentication mechanisms

3. Integration challenges

Incorporating agentic AI into both customer integrations and your own company integrations can mean significant hurdles, like:

  • Legacy system compatibility
  • API standardization and communication protocols
  • Performance optimization
  • Scalability concerns
  • Resource allocation and management

4. Regulatory compliance

The evolving regulatory landscape surrounding AI technology presents potential issues, including:

  • Adherence to emerging AI regulations
  • Cross-border compliance requirements
  • Documentation and audit trails
  • Risk assessment and mitigation
  • Regular compliance monitoring and updates

5. Performance monitoring

Maintaining and optimizing agentic AI system performance requires continuous monitoring and adjustment:

  • Real-time performance metrics
  • Quality assurance processes
  • System reliability and availability
  • Error detection and correction
  • Performance optimization strategies

These challenges highlight the complexity of implementing agentic AI systems and underscore the importance of careful planning and robust risk management strategies. Success in deploying these systems requires a comprehensive approach that addresses technical, ethical, and operational concerns, while maintaining focus on business value and user needs.

Importantly, when you partner with agentic AI vendor Quiq, our AI platform and team neutralize these challenges for you.

The future of agentic AI: Shaping tomorrow’s enterprise workflows

As we stand at the intersection of technological innovation and business transformation, agentic AI emerges as a cornerstone of future enterprise operations. But what’ll follow? Here’s what I think.

Technical evolution and integration

The future of agentic AI lies in its ability to integrate with existing enterprise systems while pushing the boundaries of what’s possible. Advanced API ecosystems and sophisticated middleware solutions are already enabling AI agents to coordinate across previously siloed systems, creating unified workflows that span entire organizations.

The next generation of agentic AI systems will feature enhanced natural language processing capabilities, enabling a more nuanced understanding of context and intent. This advancement will allow AI agents to handle increasingly complex tasks while maintaining high accuracy levels. We’re moving toward systems that can execute predefined workflows and design and optimize them in real time based on changing business conditions.

Enhancing enterprise workflows

1. Predictive process optimization

AI agents will move beyond reactive process management to predictive optimization. By analyzing patterns across millions of workflow executions, these systems will automatically identify potential bottlenecks before they occur and implement preventive measures. This capability will enable organizations to maintain peak operational efficiency while minimizing disruptions.

2. Dynamic resource allocation

The future workplace will see AI agents dynamically managing both human and technological resources. These systems will understand the strengths and limitations of different resource types, automatically routing work to optimize for efficiency, cost, and quality. This intelligent orchestration will create more flexible, resilient organizations capable of adapting to changing market conditions in real time.

3. Autonomous decision networks

As agentic AI evolves, we’ll see the emergence of decision networks where multiple AI agents collaborate to solve complex business challenges. These networks will coordinate across departments and functions, making decisions that optimize for overall business outcomes rather than departmental metrics.

Enhanced learning and adaptation

The future of agentic AI lies in its ability to learn and adapt at faster paces. Next-generation systems will feature:

1. Collective learning

AI agents will learn not just from their own experiences but from the collective experiences of all instances across an organization or industry.

2. Contextual understanding

Future systems will demonstrate deeper understanding of business context, enabling them to make more nuanced decisions that account for both explicit and implicit factors.

3. Personalization at scale

As AI agents become more sophisticated, they can deliver highly personalized experiences while maintaining operational efficiency.

Creating more resilient organizations

The evolution of agentic AI will contribute to building more resilient organizations through:

1. Adaptive workflows

Future systems will automatically adjust workflows based on changing conditions, ensuring business continuity even during unprecedented events.

2. Proactive risk management

AI agents will continuously monitor operations for potential risks, implementing preventive measures before issues arise.

3. Sustainable scaling

The future of agentic AI will enable organizations to scale operations more sustainably, automatically adjusting processes to maintain efficiency as the organization grows.

Looking ahead

While challenges around data quality, system integration, and ethical considerations persist, the trajectory of agentic AI points toward increasingly sophisticated systems. Organizations that embrace this technology and prepare for its evolution will be better positioned to:

  • Create more efficient workflows that respond to changing business needs
  • Deliver personalized experiences at scale
  • Build more resilient organizations capable of thriving in uncertain conditions
  • Drive innovation through intelligent process optimization

As we move forward, the key to success will lie not just in implementing agentic AI, but in creating organizational cultures that can effectively leverage its capabilities while maintaining human oversight and strategic direction. The future belongs to organizations that can strike this balance, using agentic AI to enhance human capabilities, rather than replace them.

We’re only beginning to scratch the surface of what’s possible. As the technology continues to evolve, it will enable new forms of business operation that are more resilient than ever before.

I love Bill’s take on this in another clip from his conversation with Kane:

Final thoughts on agentic AI and how to get started with it

Agentic AI represents a significant advancement in artificial intelligence, offering businesses the ability to automate complicated tasks while maintaining intelligence in decision-making. As organizations seek to improve efficiency and customer experience, agentic AI provides a powerful solution that goes beyond traditional automation and generative AI capabilities.

Quiq stands at the forefront of this technology, offering agentic AI solutions that help businesses improve their operations and customer interactions. With a deep understanding of both the technology and business needs, Quiq provides sophisticated AI agents that can handle complex tasks and drive the outcomes your business cares about.

4 Conversational AI Software Tools for CX and eCommerce in 2025—and How GenAI is Making Them Better

This comprehensive guide explores the four most impactful conversational AI software tools shaping the future of customer experience and eCommerce, helping businesses deliver exceptional experiences while optimizing operational efficiency.

What is conversational AI software?

Conversational AI software represents a technology stack that enables machines to understand, process, and respond to human language naturally and contextually. At its core, it combines several key components:

  • Machine Learning: Enables systems to learn from interactions and improve over time
  • Natural Language Processing (NLP): Helps computers understand human language
  • Natural Language Understanding (NLU): Interprets intent and context behind user inputs
  • Natural Language Generation (NLG): Produces human-like responses

Clearing up the confusion between conversational AI vs. GenAI vs. agentic AI: Why are we using the former term?

Before diving into specific tools, it’s essential to understand the distinction between conversational AI, agentic AI, and generative AI (GenAI). There’s a lot of confusion between these three terms, in part because of how fast AI is developing.

Conversational AI enables natural language interactions between users and systems. In contrast, agentic AI autonomously makes decisions and executes tasks. In fact, Agentic AI can independently decide what actions to take, persist in completing tasks, and adapt its approach based on outcomes—similar to how a human employee would work through a problem.

Generative AI, on the other hand, creates new content based on existing data patterns.

Think about it like this: Conversational AI is like a back-and-forth conversation between two friends, like you are used to experiencing with chatbots. You say something like “Hi, how are you?” I reply, ‘Fine, thanks, how are you?’ and we go on and on until the conversation stops.

But with GenAI, it’s similar to a ‘speak when spoken to’ situation: It’s up to you to ask me questions you want responses to. Generative experiences are typically not programmed to ask clarifying questions. And agentic AI, well, it’s a workhorse!

Nowadays, the term “conversational AI” tends to describe previous-generation technologies, while “agentic AI”, which Quiq is the leader in, is currently next-generation. We are using “conversational AI” here for a couple reasons: 1) Many people use “conversational AI” to describe AI software, even if they technically refer to GenAI or agentic AI; 2) The tools described here are not agentic, or autonomous decision-making, by default. But we’ve highlighted how generative or agentic elements make it more effective.

I want to be clear about all this because many people use these terms interchangeably, but they’re quite different, which is important. Forrester released a trends report called The State of Conversational AI, too. I’d encourage you to download and read to dive deeper into how the technology has changed and is projected to continue changing.

Okay, with that out of the way, let’s dive in.

Comparison table: Conversational AI vs. traditional chatbots

Even though conversational AI is not the latest and greatest AI out there, it’s still miles ahead of the basic chatbots of yesteryear, and the technology can still do a lot. Let’s look at a side-by-side comparison of where conversational AI elevated the previous tech.

Feature Conversational AI Traditional Chatbots
Learning Capability Continuous learning from interactions Static, rule-based responses
Language Processing Advanced natural language understanding Basic keyword matching
Contextual Understanding Maintains context across conversations Limited or no context retention
Personalization Adaptive and personalized responses Generic, pre-programmed responses
Complexity of Tasks Can handle complex queries and tasks Limited to simple, predefined tasks

Benefits of conversational AI software

By combining natural language processing with machine learning capabilities, conversational AI can provide intelligent, automated solutions that enhance both the customer experience and eCommerce business operations. Here’s a detailed look at the key benefits across different areas:

Benefits for CX

The customer experience landscape has been dramatically enhanced through conversational AI implementation. Here are the biggest benefits for CX:

  • Personalized experiences: Uses historical data and context to provide tailored recommendations and solutions.
  • Quick issue resolution: Handles common queries immediately, reducing resolution time and customer frustration.
  • Scalable support: Manages multiple conversations simultaneously, without compromising service quality.
  • Language support: Communicates in multiple languages, making services accessible to a global audience.
  • 24/7 availability: Provides instant support to customers around the clock, eliminating wait times and improving satisfaction.
  • Consistent interactions: Delivers uniform responses and maintains brand voice across all customer touch points.

Benefits for eCommerce

In the eCommerce sector, conversational AI has become a crucial tool for driving business growth and efficiency. Here are the primary benefits of conversational AI software for eCommerce:

  • Increased conversion rates: Guides customers through the purchase journey, addressing concerns in real-time to boost sales.
  • Reduced cart abandonment: Proactively engages with customers, shows exit intent, and resolves checkout issues. This proactive approach extends to reducing cart abandonment, as the AI can engage with customers showing exit intent and swiftly resolve any checkout issues that might arise.
  • Product discovery: Helps customers find relevant products through intelligent recommendations and natural conversation
  • Upselling opportunities: Suggests complementary products and premium options based on customer preferences, directly impacting revenue growth.
  • Cost efficiency: Reduces operational costs by automating routine customer interactions.
  • Data collection: Gathers valuable customer insights and shopping behavior patterns for business optimization.
  • Inventory management: Inventory management becomes more streamlined, with the AI providing real-time stock information and automated customer notifications about product availability.
  • Streamlined returns: Simplifies the returns process by guiding customers through procedures and policies.

These automated solutions continue to evolve, offering increasingly sophisticated capabilities that benefit both businesses and their customers. By implementing conversational AI, organizations can significantly improve their customer service operations, while driving sales and efficiency in their eCommerce platforms.

The 4 best conversational AI tools

Now that we’ve properly defined conversational AI and outlined the main benefits for CX and eCommerce, here are the four best conversational AI services and tools across both sectors.

Tool #1: Conversational eCommerce assistants

A conversational eCommerce assistant is a virtual tool designed to enhance the customer shopping experience by providing real-time support directly on your website through web chat or other business messaging channels. These assistants can help facilitate sales and improve customer engagement by offering a range of valuable features. Even last-generation conversational AI eCommerce assistants are equipped with capabilities such as:

  • Personalized product recommendations: Tailored suggestions based on a customer’s browsing history, preferences, or past purchases, helping them find exactly what they need.
  • Intelligent cart abandonment prevention: Proactively engaging with customers to remind them about items left in their cart and encouraging them to complete their purchase.
  • Real-time inventory updates: Ensuring customers have accurate information about product availability, reducing the chance of disappointment or frustration.
  • Seamless payment processing integration: Simplifying the checkout process with smooth and secure payment options, minimizing barriers to purchase.

However, while traditional conversational AI is effective, GenAI-powered tools take these capabilities to the next level. GenAI excels at contextualizing conversations, understanding customer needs in greater detail, and delivering offerings that feel more natural and personalized.

This enhanced ability to adapt and respond to individual shoppers makes GenAI an even more powerful tool for driving sales and creating positive customer experiences. (By the way: Check out how we’re harnessing both GenAI and agentic AI via next-generation AI agents).

Tool #2: Voice-activated AI bots

If you’re leading eCommerce, you’ve likely explored Voice Commerce, with tools like Amazon Alexa and Google Assistant leading the charge. Lots of people love the shopping experiences these robust bots offer, enhanced with AI.

Key features include:

  • Hands-free shopping experience
  • Natural language order processing
  • Voice-based product search and comparison
  • Integration with smart home devices

On the customer experience side, multimodal voice AI harnesses the latest tech in speech recognition with LLM-powered AI to create incredible, modern voice experiences with major cost reduction benefits for businesses. Not conversational AI, but impressive and worth checking out:

Tool #3: Multilingual AI chat solutions

For global businesses handling a high volume of support inquiries across multiple markets, AI-powered translation has become an invaluable tool. These solutions allow companies to break down language barriers while maintaining efficiency and quality in customer interactions. With the help of conversational AI tools, businesses can enjoy features such as:

  • Real-time translation in over 100 languages, enabling seamless communication with customers worldwide.
  • Context preservation, ensuring that the nuances and intent of conversations remain accurate across languages.
  • Automatic language detection, eliminating the need for customers to select their preferred language manually.
  • Consistent brand voice across languages, aligning your messaging and tone, no matter where your customers are located.

Thanks to advances in GenAI, these tools have evolved into far more powerful solutions, offering faster, smarter, and more accurate translations. For businesses aiming to expand globally, multilingual AI chat solutions are critical for delivering exceptional customer experiences while reducing operational challenges.

Tool #4: AI-powered training assistants

AI-powered training assistants transform the way employees learn and grow within organizations. While rule-based tools may work in certain applications, such as HR benefit matching, understanding-based tools take training to the next level by leveraging advanced AI capabilities.

These tools revolutionize employee training by offering:

  • Personalized learning paths: Tailored to each employee’s strengths, weaknesses, and learning pace, ensuring more effective skill development.
  • Real-time feedback and assessment: Providing instant insights to help employees understand their progress and areas for improvement.
  • Interactive scenario-based training: Simulating real-world situations to equip employees with practical skills and better decision-making abilities.
  • Progress tracking and reporting: Monitoring individual and team performance over time, allowing managers to identify trends and adjust strategies as needed.

According to Forrester’s report on The State of Conversational AI: “Conversational AI can reduce new-hire onboarding time from days to hours.” By combining AI technology with interactive and personalized learning, these tools enhance employee engagement and make training more impactful across various industries.

Interested in learning more? Check out how Quiq’s employee-facing AI assistants work—and discover how our technology helped one National Furniture Retailer Reduce Escalations to Human Agents by 33%.

Final thoughts on conversational AI software

Conversational AI might be a last-gen term, but conversational AI platforms can still be valuable for businesses aiming to deliver exceptional customer experiences while maintaining operational efficiency.

To stay competitive and future-proof your operations, consider strategically implementing these tools or any of their next-gen successors, starting with areas where they can provide the most immediate impact. Remember, the key to success lies in selecting the right tool, proper implementation, and continuous optimization.

If you need help with conversational AI, let us know. Or we’d be happy to get you up to speed with GenAI, and then agentic, depending on your organization’s needs.

Interested in exploring the next-generation of AI? Learn about AI Studio and agentic AI.