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What Are Agentic AI Workflows?

Posted on John Andersen

Customer expectations have outpaced the capabilities of traditional chatbots. It’s no longer enough to provide canned responses or navigate rigid decision trees. To meet today’s demands, businesses need AI systems that can think through problems, adapt in the moment, and act with context. That’s where agentic AI workflows come in.

Agentic AI workflows are changing how customer interactions happen by putting decision-making power into the hands of AI agents. With the ability to assess context, choose the best course of action, and continuously learn, these workflows go beyond answering questions. They solve problems autonomously.

How Agentic AI Workflows Work

Agentic AI workflows represent a new frontier in how generative AI is applied. Rather than focusing solely on content creation, these systems are designed to reason, make decisions, and take action based on goals and context, what the Forbes Tech Council describes as “the next frontier: in applying GenAI to real-world problem-solving.

Agentic AI workflows refer to a new generation of AI systems that go beyond pre-programmed logic. These workflows involve AI agents that perform complex tasks by:

  • Breaking goals into sub-tasks
  • Making decisions based on context
  • Iterating through tasks using real-time feedback

With traditional automation, you define what should happen and build the system to follow that exact path. Agentic workflows, on the other hand, allow AI agents to choose from a set of available tools or actions and determine the best next step based on the situation using statistical analysis. That ability to choose and to adapt is what makes them truly agentic.

That ability to make context-driven decisions enables AI agents to engage more naturally with customers, even when the conversation goes off-script. It’s about giving them a curated set of tools and letting them determine how best to move forward within defined boundaries.

Key Differences from Traditional AI

  • Static AI follows the rules; Agentic AI decides how to follow them within safeguards.
  • Traditional bots rely on pre-defined flows and intent-mapping; Agentic agents use reasoning to decide the best course of action in context, even in dynamic or multi-intent conversations.
  • Standard workflows follow a tree; Agentic workflows adjust the path based on real-time conditions.

Example of an Agentic AI Workflow

Quiq implemented an agentic AI workflow for a cosmetic services provider. The challenge? Customers entering the chat primarily wanted pricing, while the brand prioritized collecting contact information.

Traditional bots struggled with this tug-of-war. Quiq’s agentic workflow offered a better solution:

  1. Customer asks for pricing
  2. AI agent provides a broad estimate and requests contact details
  3. Customer repeats request
  4. AI Agent gives more specific pricing and again requests information

Each time, the AI agent reevaluates the context, determines how much to disclose, and how to nudge the conversation toward scheduling a consultation, all while honoring business rules.
Another example comes from a major airline using Quiq to manage both voice and digital conversations. Their AI agent can dynamically hand off to humans, escalate when needed, and make decisions on whether to collect additional details or move forward. This reduces frustrating loops and enables smoother experiences.

Key Components of Agentic AI Workflows

According to McKinsey’s 2025 AI report, companies that are scaling AI effectively are more likely to embed agents that “act autonomously and operate across workflows.”

Agentic systems require several core components to work effectively:

  • Autonomous AI Agents: These agents can operate independently but within defined limits.
  • Goal Setting: Every successful agentic workflow starts with a clear goal. Whether it’s answering a question or resolving a complex issue, agents need a target to work toward.
  • Task Decomposition: Once the goal is set, the system breaks it down into smaller steps. That way, agents can focus on one part of the problem at a time, just like people do.
  • Feedback Loops: Agents listen to what users say and adjust their approach accordingly. If something doesn’t work, they try a different path.
  • Adaptive Decision-Making: While the agents themselves don’t autonomously rewrite their logic after each interaction, the system is built for continuous improvement through Human-in-the-Loop (HITL) processes. As explained in MLJourney, HITL ensures that AI systems evolve responsibly by combining automated learning with human review. At Quiq, real-world interaction data is regularly reviewed by experts, who update Process Guides, refine prompts, and fine-tune orchestration to improve performance and reduce risk over time. For true adaptability, workflows should be LLM agnostic — giving teams the ability to swap models as performance or cost factors change
  • Environment Interaction: Agents call APIs, fetch data, and orchestrate responses using tools like Quiq’s AI Studio.

The Process Guide serves as a digital playbook, providing succinct instructions that guide the AI agent through various types of conversations. When combined with company-specific data and the right toolset, it provides the foundation for how agents behave and make decisions in real time.

Flowchart showing how agentic AI breaks a complex goal into sub-tasks, including interpreting intent, selecting tools, and adapting to input
Agentic AI workflows use task decomposition to solve problems through a series of context-driven, autonomous decisions, constantly refined through real-time feedback.

Types of AI Agent Workflows

Agentic workflows aren’t one-size-fits-all. They come in various forms, depending on the use case, business goals, and the level of decision-making required. Some common types include:

  • Conversational AI Agents: These agents handle interactions in voice or chat. Unlike scripted bots, they understand context, manage multi-turn conversations, and adjust based on what the customer says, even if they go off-topic.
  • Multi-Agent Systems: In more complex settings, multiple AI agents—each an expert in a specific business domain—work together to solve a problem. For example, a “Retail Agent” could handle a customer’s initial product questions, then seamlessly hand off the conversation to a “Logistics Agent” to track the package and adjust the delivery. Together, they collaborate in real time to reach a shared goal that spans different business functions.
  • Robotic Process Automation with Agency: RPA has traditionally followed static rules, but when infused with agentic logic, it becomes much more responsive. These agents determine when and how to execute processes based on the current context, rather than just a script.

Benefits of AI Agentic Workflows

According to Gartner’s 2024 Hype Cycle for Artificial Intelligence, agentic AI is nearing the peak of inflated expectations, with enterprise readiness expected within the next 2 to 5 years. This signals growing momentum and urgency for companies to invest in agentic systems now.

Agentic workflows offer CX teams a smarter, more adaptable way to engage with customers:

  • Increased Efficiency: They handle both routine and complex tasks, freeing up your human agents to focus on what really needs their attention.
  • Scalability: As customer demand grows, agentic systems scale with you, eliminating the need to rebuild workflows every time something changes.
  • Improved Accuracy: Over time, agents start to notice patterns. They become better at identifying familiar issues and knowing how to respond, which results in fewer mistakes and more helpful interactions.
  • Faster Resolution: When a customer asks a question, the agent doesn’t just follow a script; instead, the agent provides a personalized response tailored to the customer’s needs. It looks at the full context and decides what to do next, making it easier for people to get what they need without delay.
  • Cost Savings: By automating more intelligently, teams reduce repetitive tasks and free up support staff to focus on more complex, higher-impact work.
  • Better Experience: Conversations feel more natural, responsive, and helpful, not robotic or rigid.

What makes agentic workflows stand out is their flexibility. Instead of sticking to a script, they respond to what the customer is actually saying. If topics change mid-conversation, the AI can adjust its approach and keep moving toward a resolution, just like a good customer service rep would. The AI can also refer to previous topics mentioned in the same conversation. Further, the AI can handle a single request from the user that contains more than one instruction.

Challenges of Agentic AI Workflows

Agentic AI systems introduce powerful capabilities, but they also require thoughtful planning and oversight. Some common hurdles include:

  • Data Availability: Without access to accurate data, even the smartest agent can hit a wall. Data gaps make it harder to respond accurately or take meaningful action.
  • Complex Integration: Connecting AI agents to legacy systems, third-party platforms, and internal tools often requires customized API work and thorough testing.
  • Ethical Oversight: It’s not just about whether an agent can do something; it’s about whether it should be done. Guardrails must be in place to prevent false promises or unintended outcomes.
  • Guardrails and Governance: AI agents require a framework that specifies what is permitted. This includes limits on actions, boundaries for language, and checks on tone and accuracy.
  • Variability in AI Models: Large language models can behave unpredictably. Without frequent calibration and prompt refinement, testing, and monitoring, output quality can drift over time.

To help businesses stay in control, Quiq uses a layered system of checks and balances. Every AI agent response is evaluated before and after it’s delivered to the user, ensuring that only approved, on-brand, and contextually appropriate messages are sent. This provides organizations with confidence that their AI agents act responsibly and remain aligned with business goals through a layered system of checks and balances.

How to Implement Agentic AI Workflows

Rolling out agentic AI workflows doesn’t have to be overwhelming, especially with the right framework in place. At Quiq, implementation centers on clarity, control, and collaboration at every step:

1. Define the Problem: Start by identifying the customer experience gap you aim to solve. This ensures the agent is aligned with actual business needs.

2. Establish Goals: Set measurable success metrics and clear behavioral boundaries for the AI agent to work within. Crucially, define which decisions are safe for the AI to make autonomously and which require human oversight.

3. Design Workflows and Human Touchpoints: Create Process Guides that serve as coaching instructions. These guides must not only direct the AI but also explicitly define the human-in-the-middle interaction points:

    • Escalation Triggers: When should the agent automatically hand off to a human (e.g., customer frustration, complex out-of-scope query)?
    • Approval Gates: What critical actions (e.g., processing a large refund, modifying an account) must a human approve before the AI executes them?
    • Collaborative Inputs: Where should the AI pause and ask a human for a specific piece of information or a judgment call before proceeding?

4. Set Up Data Access: Connect the agent to relevant APIs, real-time customer data, and your company knowledge base so it can take meaningful action.

5. Test Rigorously: Use conversation simulations with defined assertions to validate expected behavior. This phase must include both automated testing and thorough human review to check for nuance, tone, and brand alignment that automated tests might miss.

6. Deploy and Iterate: Once live, monitor performance closely and refine as needed. Regular reviews ensure the agent continues to meet both user expectations and business standards.

Quiq’s AI Studio serves as the orchestration layer behind it all, connecting to any LLM, integrating with your systems, and offering platform-agnostic flexibility. With built-in testing and ethical safeguards, teams can launch with confidence and scale intelligently, integrating with any LLM or custom model and supporting platform-agnostic deployments.

Use Cases Across Industries

Agentic AI workflows can be applied in nearly every industry where real-time decisions, context awareness, and dynamic action matter. Here are a few examples of how different sectors are putting them to work:

  • Customer Service: Agents do more than deflect calls. They resolve tickets, identify customer sentiment mid-conversation, and escalate issues when needed without waiting for human intervention.
  • Retail & eCommerce: From “Where is my order?” inquiries to adjusting delivery addresses and cross-selling accessories, agentic workflows help retailers respond faster, reduce cart abandonment, and personalize experiences.
  • Airlines: Agents handle everything from booking changes to baggage tracking. They can ask clarifying questions, navigate loyalty programs, and escalate only when absolutely necessary across both voice and chat.
  • Healthcare: Patient interactions require a balance of caution and efficiency. Agentic workflows assist with triage, scheduling, and responding to common questions based on historical data, while flagging sensitive cases for human review.
  • Financial Services: AI agents assist with account management, fraud alerts, loan application updates, and more while ensuring all actions stay within compliance frameworks.

In every case, the AI agent isn’t just reacting; it’s analyzing the context, choosing the right tools, and proactively guiding each interaction toward a resolution.

Moving Forward with Agentic AI

Agentic AI workflows represent a meaningful leap forward in automation. Rather than relying on rigid logic, these systems adapt, make decisions, and deliver value in real time.

AI doesn’t have to be intimidating. The best way to begin is by identifying one problem worth solving, building a focused solution around it, and expanding from there.

With Quiq’s orchestration tools, business-ready data, and ethical guardrails, organizations can confidently embrace agentic AI.

Ready to get started?

  • Schedule a Demo: See how agentic workflows operate in real time with Quiq’s AI Studio.
  • Talk to a Solutions Expert: Let us help you identify the highest-impact use case for your business.
  • Explore Quiq’s AI Agents: See how our AI Agents operate autonomously within smart guardrails to solve customer problems quickly and accurately.

Agentic AI: Tangible Use Cases to Bring the Art of the Possible to Life

Posted on Lauren Winder

Gartner recently predicted that agentic AI will automatically resolve 80% of common customer service issues by 2029. Sounds exciting, right? But you may also be asking yourself…’what exactly would this look like?’

The possibilities of agentic AI — a type of AI designed to autonomously execute tasks, make decisions, and take action, all while adapting to evolving conditions in real time — are practically endless. And it’s easy to feel overwhelmed by too many options. In fact, that’s why geniuses like Einstein and Obama prefer to have closets full of the same color clothes!

So rather than letting your imagination run wild with hypothetical scenarios, we’ve decided to highlight a few transformative and totally tangible agentic AI applications used by real CX teams. Because choosing the right agentic AI use case for your CX organization is much more critical than deciding what shirt to wear. Or, at least we think so.

Orchestrate Reward Programs Across Systems

This use case involves an AI agent that can combine knowledge of loyalty program policies with specific customer data from across systems to provide highly personalized service and rewards. It can dynamically automate key decisions and processes to issue credit, offer discounts, upgrade shipping, and more based on a customer’s tenure, rewards card status, loyalty points tier, purchase history, return frequency, etc. Any actions taken or additional data points gathered are recorded in the appropriate systems to help eliminate information silos.

What Makes This Agentic

In addition to offering custom, bi-directional integrations with other CX tools, true agentic AI platforms provide an orchestration layer that guides the conversation flow. The AI agent leverages various Process Guides to dynamically adapt and respond to users’ questions.

These Process Guides provide general instructions for handling particular tasks, while also specifying which tools and knowledge the agent can invoke when appropriate. In this refund scenario, the AI agent can:

  • Look up customer information, including order status and membership level
  • Access knowledge bases containing return and warranty policies
  • Generate a personalized response based on all available context
  • Initiate the return process on the customer’s behalf

Rather than relying on predefined, rigid if/then logic, the AI agent determines which actions to take based on conversational context. Every interaction passes through a series of pre- and post-generation guardrails that combine LLMs, business rules, and other validation mechanisms to ensure the exchange is appropriate, on-brand, accurate, and genuinely resolves the customer’s issue.

Diagnosis and Routing via Image Recognition

In this scenario, the agentic AI agent is used to accurately diagnose product issues and route customers to the appropriate human agents for troubleshooting. The AI agent does this by leveraging sophisticated image recognition and combining it with both general product and account-specific information gathered during the conversation and from across other systems.

What Makes This Agentic

Not only does this AI agent use the communication and reasoning power of LLMs to fully comprehend language and users’ inquiries, but it also leverages image processing as part of its Process Guide. It’s similar to how human technicians are taught to know when to ask for an image and look for visual cues to accurately diagnose an issue. The AI agent can then factor this knowledge into the context of the conversation and take the appropriate next step by escalating the customer to the right human agent.

The best part? All the information the AI agent collects over the course of the conversation, including the image and data accessed from other systems, is passed to the human agent at the time of escalation. In addition to receiving an AI-generated summary, the human agent is also able to read back over the full conversation in detail. This seamless handoff ensures the customer never has to repeat themselves, and significantly accelerating time to resolution.

Proactive Service Recovery

Using this agentic AI service enables travel and hospitality companies to detect customer patterns that typically lead to negative experiences based on historical data, and proactively address them to improve guests’ experiences. For example, imagine a guest arrives late, the hotel restaurant is closed, and the pool cabana they want is sold out the next day.

A front desk employee may receive an automated email alert flagging these issues due to the effects they had on previous guests. To proactively improve this customer’s experience and increase the chances they will re-book with the hotel in the future, it’s recommended they bring a bottle of wine and hand-written thank you note up to the guest’s room. Or, the agentic AI service could automatically address these concerns by text messaging a coupon for a free wine bottle to the guest directly.

Example Agentic AI Workflow

Discover More Agentic AI Use Cases

Did you find these agentic AI use case examples insightful? We’re just getting started! Our latest guide is meant to help bring the art of the possible to life by exploring:

  • Agentic AI applications across three key industries: Retail (including eCommerce), Travel & Hospitality, and Consumer Services
  • Nine increasingly complex use cases, including a review and feedback analysis engine, cross-channel service orchestration, and many more
  • Real success stories from a top furniture retailer and other early adopters of agentic AI for CX

Download now

5 Agentic AI Examples and Use Cases

Posted on Mark Kowal

Key Takeaways

  • Agentic AI acts autonomously – Unlike rule-based bots, it reasons, adapts, and executes tasks with minimal human input.
  • Real business impact – Top use cases include customer service, sales, and internal automation, driving faster resolutions and measurable efficiency gains.
  • Human + AI collaboration wins – Start small, use AI to offload repetitive work, and scale once a clear ROI is proven.
  • Scale strategically – Begin with high-impact use cases, refine workflows, and expand as systems and teams mature.

Agentic AI is poised to have a massive impact on businesses. Unlike traditional AI systems that do simple answer generation from a knowledge base, agentic AI takes things further—it possesses the ability to act autonomously, learn from interactions, and make independent decisions to achieve specified goals.

This advanced form of AI goes beyond basic automation, offering adaptive and intelligent solutions that can improve how organizations operate and deliver value to their customers. Let’s explore how agentic AI is reshaping industries and creating new opportunities for growth.

Agentic AI Use Cases Across Industries

There’s a lot of momentum behind agentic AI throughout many industries, with even more use cases therein, so this won’t be an exhaustive list. Still, here’s where I see the most exciting agentic AI use cases right now.

1. Customer service and support

This is one of our main focuses here at Quiq. Agentic AI is improving pre- and post-sale customer service by automating repetitive, time-consuming tasks without losing the human touch that today’s customers demand. Unlike traditional chatbots that follow rigid scripts, these systems understand context and provide natural, human-like responses.

Here’s how:

Customer-facing AI agents

AI agents go beyond FAQs to handle Tier 1 inquiries by offering nuanced, conversational support. They can understand the context of a conversation, the appropriate time to help a customer self solve, and when to escalate to another team member.

Here are customer journey moments across pre- and post-sales service and support that we’re finding most effective to apply agentic AI agents to:

Pre-sale customer service

  • Product selection (Web/Mobile)
  • Product or service configuration
  • Place an order
  • Purchase and schedule a service
  • Product selection shopping cart (AI Agent suggests other products that complement products already in a shopping)

Post-sale customer support

  • Answer a question with information (using knowledge bases, product descriptions, product catalogs, etc.)
  • Order statuses
  • Proactive order status notification
  • Order returns, changes/corrections, and exchanges
  • Order/service delivery change (Shipper, installation, in person required)
  • Subscription managements
  • Loyalty program, points and/or gift card balance
  • Break fix/troubleshoot issues

Agent-facing and employee-facing AI assistants

At the contact center level, there’s several high-value applications of agentic AI to support human agents, from suggesting responses based on company/user info, to automating routine processes, like checking a bag, to checking for things like professional tone and spelling. If you’re interested in all the ways you can get started adopting next-gen AI for contact centers, watch our recent webinar on this topic, From Contact Center to Agentic AI Leader: Embracing AI to Upgrade CX.

Agentic AI can also aid other employees, outside of contact center agents. For example, we worked with one office supply retailer to empower their in-store sales associates with an AI assistant that provides fast answers to customer questions. And another, high-profile carpet retailer in Europe uses a Quiq-powered AI assistant to help onboard and train their employees.

Workflow automation

Outside of automating and improving conversations – whether it’s full automation via an AI agent, or whether via augmenting your human agents – there’s a whole host of other business processes and workflows that can benefit from agentic AI and LLMs more broadly.

Think everything from delivering better semantic search to users on your website (either product or knowledge base search), to automatically classifying and grading every customer interaction with your business (measured with metrics like CSAT).

Workflow automations can enable businesses to leverage the power of agentic AI and LLMs on demand to improve processes and customer touch points across their entire organization, not just during a conversation.

The result: Better containment and resolution rates, and customer effort scores (CES) if a customer is escalated to a human agent. Reduced average handle time (AHT), more consistent service quality, satisfied customers, and support teams empowered to focus on complex, high-value tasks.

2. Sales and account prospecting

Traditional sales outreach has always been a numbers game, with teams spending countless hours on manual prospect research and outreach. Agentic AI is changing this landscape by automating the most time-intensive aspects of prospecting while making interactions more personalized and effective than ever before.

Here’s how:

  • Intelligent lead scoring: Advanced algorithms analyze vast datasets of customer behaviors, interactions, and market signals to automatically identify and prioritize the most promising leads, allowing sales teams to focus their energy where it matters most.
  • Data-driven personalization: AI agents craft highly tailored outreach campaigns by synthesizing prospect data, past interactions, and industry trends to create messaging that resonates on an individual level.
  • Automated account management: Proactive monitoring of customer accounts to predict churn risks, identify up-sell opportunities, and maintain engagement through automated but personalized touch points.
  • Real-time sales intelligence: AI-powered dashboards provide sales representatives with actionable insights about prospect behavior, helping them make informed decisions about when and how to engage.
  • Multi-channel engagement optimization: Smart analysis of prospect engagement patterns across channels to determine the optimal timing, medium, and message for each interaction.
  • Predictive pipeline management: Advanced forecasting capabilities that help sales teams anticipate deals at risk and identify which opportunities are most likely to close.

The result: Sales teams can see higher conversion rates, reduce time spent on manual prospecting, and have more meaningful customer relationships built on data-driven insights rather than gut feelings. This optimizes sales cycles and leads to increased revenue, and sales representatives who can focus on what they do best: building relationships and closing deals.

3. Supply chain and logistics

Today’s supply chains demand solutions that can process vast amounts of data and make split-second decisions. Agentic AI is improving this space by creating self-optimizing supply chains that can predict, adapt, and respond to changes in real-time, far beyond what traditional automation could achieve.

Here’s how:

  • Predictive demand analysis: Advanced AI models process historical data, market trends, and external factors (like weather patterns or social media sentiment) to forecast demand with better accuracy, helping businesses stay ahead of market shifts.
  • Intelligent route optimization: Real-time analysis of traffic patterns, weather conditions, and delivery windows to automatically determine the most efficient delivery routes, reducing both costs and environmental impact.
  • Dynamic inventory management: AI-powered systems that continuously monitor stock levels across locations, automatically adjusting ordering patterns based on demand fluctuations, and preventing costly stock-outs or overstock situations.
  • Supplier risk assessment: Continuous monitoring of supplier performance, market conditions, and global events to identify potential disruptions before they impact operations, allowing for proactive mitigation strategies.
  • Automated procurement intelligence: Smart systems that analyze market prices, supplier performance, and internal needs to automatically trigger purchases at optimal times and prices.
  • Predictive maintenance scheduling: AI agents that monitor equipment performance and predict maintenance needs before failures occur, minimizing costly downtime.

The result: Companies achieve more efficient supply chains with reduced operational costs, improved delivery times, and enhanced customer satisfaction. Benefits include more resilient operations, better inventory management, and a significant competitive advantage in the market.

4. IT operations and workflow automation

Agentic AI is recasting IT operations by creating systems that can predict, prevent, and resolve issues autonomously, fundamentally changing how organizations manage their technical infrastructure.

Here’s how:

  • Intelligent system monitoring: AI agents continuously analyze system performance metrics, user behavior patterns, and potential security threats across the entire IT infrastructure, providing insights to employees and automated responses to emerging issues.
  • Predictive problem resolution: Advanced algorithms identify potential system failures or bottlenecks before they impact operations, automatically implementing fixes or alerting IT teams with detailed solution recommendations.
  • Automated security management: Real-time threat detection and response capabilities that go beyond traditional rule-based systems, learning from new attack patterns and automatically implementing defensive measures for the team.
  • Smart resource allocation: Dynamic adjustment of computing resources based on actual usage patterns and predicted demand spikes, ensuring optimal performance while minimizing costs.
  • Workflow intelligence: AI-powered analysis of business processes to identify bottlenecks, suggest improvements, and automatically implement optimizations where possible.
  • Self-service enhancement: Intelligent AI assistants that can handle routine IT requests and troubleshooting, learning from each interaction to improve future responses.

The result: Organizations experience significantly reduced system downtime, faster issue resolution, and more efficient resource utilization. IT teams can shift their focus from routine maintenance to strategic initiatives, while employees enjoy more reliable systems and faster support response times.

5. Marketing personalization

Gone are the days of one-size-fits-all marketing campaigns. Agentic AI is enhancing how brands connect with their audiences by enabling true one-to-one personalization at scale, upleveling generic messaging into highly targeted, contextually relevant experiences that evolve in real-time based on customer behavior.

Here’s how:

  • Cross-channel personalization: Intelligent systems that maintain consistent, personalized messaging across all customer touch points while adapting to channel-specific requirements and user preferences.
  • Predictive journey mapping: Advanced analytics that anticipate customer needs and automatically adjust marketing touchpoints, ensuring the right message reaches the right person at the optimal moment in their journey.
  • Campaign optimization: Continuous monitoring and automatic adjustment of campaign parameters, creative elements, and targeting criteria to maximize performance without human intervention.
  • Smarter budget allocation: AI-driven analysis of campaign performance that automatically redistributes marketing spend to the highest-performing channels and audiences in real-time.
  • Behavioral intent analysis: Sophisticated processing of customer interactions to predict future behaviors and automatically trigger relevant marketing actions before customers even express their needs.

The result: Marketing teams can achieve higher engagement rates, conversion rates, and better ROI on their marketing investments. Customers receive more relevant, timely communications that actually add value to their experience, leading to increased brand loyalty and CLV (Customer Lifetime Value).

Agentic AI use cases in four key industries

Agentic AI is improving direct-to-consumer interactions by creating personalized, efficient, and seamless experiences across multiple sectors. Here’s a detailed examination of how agentic AI affects four key industries:

1. Retail

Here’s how retail is being redefined by AI to create hyper personalized shopping experiences in channels like eCommerce while streamlining operations:

  • Proactive and personalized shopping assistance: AI agents that provide proactive, real-time advice and product recommendations based on individual preferences and past purchases.
  • Customer service automation: Intelligent AI agents that handle inquiries, returns, and provide product information 24/7.
  • Cart abandonment prevention: Smart systems that identify and address potential checkout issues before they lead to abandonment.

The result: Higher conversion rates, reduced cart abandonment, better CSAT, enhanced resolution rates, and improved customer loyalty through AI-driven shopping experiences.

Learn how a national furniture retailer reduced escalations to human agents by 33% with Quiq. Get case study >

2. Travel

Here’s how the travel industry is leveraging agentic AI to create seamless journeys:

  • Real-time travel assistance: Smart systems that provide customers with on-the-go support and recommendations during trips.
  • Personalized experiences: AI-driven recommendations to customers for activities and experiences at destinations.
  • Intelligent trip planning: AI agents that create customized itineraries based on preferences, budget, and travel history.
  • Price prediction: Advanced algorithms that forecast flight and hotel prices to recommend optimal booking times to the customers.
  • Disruption management: Automated systems that predict and respond to customers’ travel disruptions with alternative solutions.

The result: More satisfying and efficient travel experiences, with fewer disruptions and better value for travelers.

3. Hospitality

Here’s how agentic AI has enabled hotels and restaurants to deliver superior service while improving operational efficiency:

  • Smart concierge services: AI agents that provide 24/7 guest support and personalized recommendations.
  • Room customization: Automated systems that adjust room settings based on guest preferences.
  • Schedule optimization: Intelligent back-of-house systems that manage staffing, inventory, and maintenance schedules.
  • Guest experience prediction: AI analysis of guest data to anticipate needs and prevent issues.

The result: Enhanced guest experiences, improved operational efficiency, and higher satisfaction rates across all service touchpoints.

Check out how Accor doubled intent-to-book metrics with Quiq’s AI. Read case study >

4. Financial services

Here’s how AI agents are revitalizing financial services by delivering 1:1 financial guidance and automated wealth management solutions:

  • Personal financial management: AI-powered advisors that provide customers with customized investment strategies and budgeting recommendations based on individual financial goals and risk tolerance.
  • Investment automation: Smart portfolio management systems that automatically rebalance and optimize investments for customers.
  • Fraud prevention: Intelligent behind-the-scenes systems that detect and prevent unauthorized transactions in real-time.
  • Credit decisioning: Automated assessment of creditworthiness using alternative data points and behavioral patterns.

The result: More accessible financial services, improved security, and personalized wealth management solutions for consumers at all levels.

Agentic AI Final Thoughts

Agentic AI offers exciting opportunities for efficiency, innovation, and growth. Those who embrace agentic AI will find themselves better positioned to meet evolving customer expectations and market demands.

The technology’s ability to automate complex tasks while maintaining a human touch, as demonstrated by what you can build in Quiq’s AI Studio platform, showcases its potential to revitalize business operations across industries. From customer service to sales, from supply chains to marketing, agentic AI is proving its value in driving business success.

To stay competitive and support consumers’ growing preference for quick self-service resolutions, organizations must consider how agentic AI can enhance their operations and drive growth. Get in touch with us today for a demo on how Quiq’s agentic AI can help your business move the CX metrics that matter most to you.

Frequently Asked Questions (FAQs)

What is agentic AI?

Agentic AI refers to systems that can make decisions, take initiative, and complete tasks with minimal human supervision. Unlike traditional chatbots or automation tools, agentic AI reasons, adapts, and acts based on context.

How is agentic AI different from generative AI?

Generative AI creates content like text or images, while agentic AI uses that intelligence to take action. Think of it as the difference between writing an email and sending it to the right person at the right time.

What are some common use cases for agentic AI?

Top examples include customer support automation, personalized sales outreach, workflow optimization, and IT process management –  all areas where context and decision-making matter.

How does agentic AI improve customer experience?

It enables faster resolutions, consistent messaging, and proactive service – often predicting what a customer needs before they ask. That means shorter wait times and smoother interactions.

Where should businesses start with agentic AI?

Start small. Identify one or two high-value use cases tied to measurable outcomes like efficiency or satisfaction, test and refine the workflow, then scale across teams once results are proven.

Unlock Agent Potential with Quiq’s Real-Time Agent Assist Capabilities

Posted on Max Fortis

Customer service is evolving, and with it, the demands placed on service agents are rapidly increasing. From managing complex inquiries to delivering personalized, high-quality customer experiences, agents are under constant pressure to perform at their best. This is where Quiq’s Real-Time Agent Assist comes into play. With AI-driven insights, real-time guidance, and cutting-edge automation, this powerful tool doesn’t just support agents—it transforms them into top performers.

In this blog, we’ll explore precisely how Quiq’s real-time agent assist capabilities—part of our overall AI contact center offering—can revolutionize your customer service operations by boosting efficiency, reducing costs, and delighting customers.

Transform agent productivity with real-time AI insights

Agents are at the heart of your customer interactions, and giving them the tools they need to succeed can make all the difference. Quiq’s real-time agent assist AI is designed to empower agents with in-the-moment guidance and actionable insights during live interactions. These agent tools mean faster resolutions, greater confidence, and improved productivity for your team.

With Quiq, agents no longer have to second-guess their responses or scramble to find the right information. Instead, AI steps in to provide precise recommendations and cues at just the right time.

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Experience the future of customer service firsthand. Get a demo of Quiq’s real-time agent assist offering today and see how it can transform your support team.

AI-powered efficiency for every role, every conversation

Whether it’s advising agents on complex issues, streamlining onboarding, or cutting operational costs, Quiq’s real-time agent assist offering delivers impactful benefits across the board.

Here’s how it works for your business:

1. Optimize decision-making

Equip your agents with real-time insights and recommended actions, enabling them to resolve issues with precision. Whether handling a challenging customer inquiry or upselling products,

Quiq ensures that agents make the best decisions in every interaction. Agents get real-time suggested responses as the conversation progresses, which leverage the same underlying knowledge and systems that power AI agents. Think: knowledge bases, product catalogs, CRM data, and any other data sources that might be helpful in the context of agentic AI systems. AI Assistants don’t just suggest responses; they can also act on an agent’s behalf—like automatically starting a warranty claim, or updating a customer’s flight, without making the agent do the work manually.

2. Streamline training and onboarding

AI-powered coaching is a game changer for new agents. With Quiq, your team gains access to on-the-job guidance that accelerates learning. New hires ramp up faster, while experienced agents refine their skills, creating a consistently high-performing team. New agents get the same great suggested responses and actions that a high-performing human or AI agent would have.

It makes a brand-new agent as good as an AI agent, because they’re working off the same datasets, integrations and responses.

3. Reduce operational costs

Achieve more with fewer resources. Quiq automates routine inquiries and streamlines workflows, freeing up your agents to focus on high-value interactions. This means fewer hiring needs and a leaner operational model. In addition, AI Assistants can gather extra key pieces of data during a conversation, add them to specific ticket fields or append them to a case or conversation, reducing the amount of manual entry an agent has to do.

4. Enhance customer satisfaction

Quiq’s agent-facing AI empowers agents to provide accurate, instant, and personalized support, leading to faster resolutions and happier customers. The result? Higher CSAT scores and stronger customer loyalty. This is done through a combination of response suggestions, real time feedback, and taking action on the agent’s behalf.

5. Insights into agent performance

Quiq’s robust agent analytics give contact center leaders deep insight into how human agents are performing. In our experience, this is critical to ensuring that real-time agent assistance does its job and helps agents in the most effective way possible.

Watch this video to learn how it works >

Key features of real-time agent assist with Quiq

At the core of Quiq’s real-time agent assist lies a suite of innovative features designed for seamless customer interactions. See it in action:

1. In-the-moment guidance and coaching

Built in Quiq’s AI Studio, AI assistants can leverage data from any enterprise system and combine that with conversational context to suggest responses and provide recommendations, or coaching, during a conversation. Agents thrive with support that adapts in real time. Quiq provides targeted coaching during live conversations, using AI to deliver hints, reminders, and workflows tailored to each interaction.

For instance, in a case study with an office supply retailer, Quiq’s assist feature was so effective it allowed associates to get immediate answers to questions 2 out of 3 times. This led to a whopping 68% self-service rate resolution rate.

2. Automated post-conversation summary and analysis

After-conversation work can be a major time sink—but not with Quiq. Using AI-generated summaries, agents can cut down on post-interaction tasks, allowing them to focus on the next customer. Customers get faster service, and agents stay productive.

Importantly, summaries are also available for the agent right when they take over a conversation. For example, if the user has been talking with an AI agent, the human agent will get a summary of the conversation, creating a seamless experience for the end customer.

Beyond summarization, Quiq can also extract key pieces of information and automatically update CRMs or other enterprise systems with the appropriate information.

3. Smart routing and prioritization

Not all customer inquiries are created equal. Quiq’s intelligent routing ensures that inquiries are directed to the best-suited agents based on real-time data like expertise, workload, or customer urgency. This minimizes wait times and optimizes outcomes.

Real results with AI assistants: Office supplier case study

When a leading office supply retailer integrated Quiq’s agent-facing AI Assistant, they saw impressive improvements in just a few weeks.

  • Increase in containment rates: 35% (with a 6-month average containment rate of 65%)
  • Associates got immediate answers: 2 out of 3 times
  • Self-service resolution rate: 68%
  • Associate satisfaction with AI: 4.82 out of 5

The AI ensured that each employee was guided toward resolving customer issues promptly while automating laborious and repetitive inquiries. This created a win-win for both customers and the team itself. Read full case study >

Elevate customer support with Quiq’s real-time agent assist offering

Imagine a team where every agent operates at their peak potential, guided by AI that backs their every move. Quiq’s real-time agent assist isn’t just an upgrade for your service department—it’s a revolution that touches every part of your customer experience.

If you’re ready to unlock your agents’ potential and take your customer service to the next level, now is the time to act.

AI Agent Evaluation: Ten Questions to Ask to Determine if It’s Time to Upgrade

Posted on Max Fortis

Key Takeaways

  • A capable AI agent should interpret multi-part questions and provide a single, cohesive answer rather than treating each part separately.
  • AI agents should remember previous turns, handle follow-ups naturally, and resume earlier topics without losing track.
  • The best AI agents connect with backend systems (CRM, order data, account info) to take action, not just provide static replies.
  • A reliable agent avoids hallucinations by escalating or deferring when unsure instead of guessing.
  • When escalation is needed, the agent should pass the full context so the customer doesn’t have to repeat themselves.

Keeping up with AI isn’t easy, and teams certainly can’t drop everything for every little update. However, there are times when failure to update your AI for CX tools can have a major impact on your customer experience and brand trust. And the rise of agentic AI is one of those times.

Cutting-edge AI agents combine the reasoning and communication power of large language models (LLMs), generative AI (GenAI), and agentic AI to understand the meaning and context of a user’s inquiry or need, and then generate an accurate, personalized, and on-brand response — often proactively and autonomously.

But even many self-proclaimed “agentic AI” vendors fail to offer their clients truly next-generation AI agents, since the models and technologies behind them have gone through such a rapid series of updates in such a short period of time. So how do you know if your AI agent is current and whether it’s time for an update?

That’s where this AI agent evaluation comes in. We’ve created a series of questions CX leaders can ask the AI agents on their companies’ websites to gauge just how advanced they really are, and how urgently an update is needed. Already considering a new agentic AI platform? Asking your top vendors’ customers’ AI agents these questions can also help streamline the selection process.

Simply give yourself a point for each of the ten questions the AI agent answers effectively, and half a point for each bonus question. Note that you may tailor the questions if they don’t make sense in the context of a particular product or service. Then, total up your points, and read on for your results and recommended next steps. Are you ready?

Question #1: “What is your return policy and do you offer exchanges?”

Add a Point If…

The AI agent answers both of these questions in a single, comprehensive response. Ideally, it also sends a link to the relevant knowledge base articles referenced in the answer.

Question #1

No Points If…

The AI agent provides an answer for only one of these questions and fails to answer the other.

This is a leading indicator of first-generation AI that attempts to match a user’s intent to a specific, pre-defined query and “correct” response. In contrast, a next-generation AI agent can comprehend the entirety of a user’s question, identify all relevant knowledge, and combine it to craft a complete response.

Question #2: “Do you offer financing? How do I qualify?”

Add a Point If…

The AI agent uses the context from the first question to understand the second one, and provides a single, comprehensive, and adequate response for both.

No Points If…

The AI agent either sends you an unrelated response, or replies that it is unable to help you, and offers to escalate to an agent.

This is another sign that the AI agent is attempting to isolate the user’s intent to provide a specific, matching response, rather than understanding the context of the conversation and tailoring its response accordingly. In some cases, the AI agent may actually harness an LLM to generate a response from a knowledge base. But because it uses the same outdated, intent-based process to determine the user’s request in the first place, the LLM will still struggle to provide a sufficient, appropriate response.

Question #3: “Can you help me track my order?”

Add a Point If…

You are currently logged into the site (or the AI agent is able to automatically authenticate you using your phone number, for example) and the AI agent immediately identifies you and finds your order. If you are not logged in, add a point if the AI agent asks for your information and can quickly locate your account to help you with your order.

Question #3

No Points If…

The AI agent immediately sends you to a human agent to help with your request — regardless of whether you are logged into the site.

This means the AI agent operates in a silo and does not have access to other CX systems outside of a knowledge base, leaving it unable to provide anything other than general information and basic company policies. The latest and greatest agentic AI platforms integrate directly with the other tools in the CX tech stack to ensure AI agents have secure access to the customer information they need to provide personalized assistance.

Question #4: “Can you help me track my order? My order number is [insert order number] and my email is [insert email address].”

Add a Point If…

The AI agent immediately finds your order and provides you with a tracking update, without asking you to repeat any of the information you included in your original message.

No Points If…

The AI agent agrees to help you track your order, but says it needs the information you already provided, and asks you to repeat your order number and/or email.

First-generation AI agents are “programmed” to follow rigid, predefined paths to collect the details they have been told are necessary to answer certain questions — even if a user proactively provides this information. In contrast, cutting-edge AI agents will factor all provided information into the context of the larger conversation to resolve the user’s issue as quickly as possible, rather than continuing to force them down a step-by-step path and ask unnecessary disambiguating questions.

Question #5: “Can you help me track my order? I don’t want it anymore and would like to start a return. / Does store credit expire?”

Add a Point If…

After answering your first question, the AI agent responds to your second, unrelated follow-up question, and then automatically brings the conversation back to the original topic of making a return.

Question #5

No Points If…

After answering your first question, the AI agent responds to your second, unrelated follow-up question, but never returns to the original topic of conversation.

This is another indicator that the AI agent is relying on predefined user intents and rigid conversation flows to answer questions. A truly agentic AI agent can respond to a user’s follow-up question without losing sight of the original inquiry, providing answers and maintaining the flow of the conversation while still collecting the information it needs to solve the original issue.

Question #6: “Are you able to recommend an accessory to go with this [insert item]?”

Add a Point If…

The AI agent sends you a list of products that are complementary to the original item. Ideally, it sends a carousel of photos of these items with buttons to add them to your cart directly within the chat window.

No Points If…

The AI agent immediately escalates you to a human agent. Subtract a point if the agent is in support, not sales!

This scenario occurs when an AI for CX platform is built to support post-sales activities only, and lacks the ability to route users to the appropriate human agent based on the context of the conversation. This results in missed revenue opportunities and makes it difficult to measure and improve customers’ paths to conversion. The latest agentic AI solutions, however, support both the services and sales side of the CX coin by integrating with teams’ product catalogs, offering intelligent routing capabilities, and more

Question #7: “Why is the sky blue?”

Add a Point If…

The AI agent politely refuses to answer your question by acknowledging this topic falls outside its purview, and then informs you about the type of assistance it’s able to provide.

Question #7

No Points If…

The AI agent attempts to answer this question in any way, shape, or form — even if its response is correct.

In this situation, the AI agent lacks the pre-answer generation checks that cutting-edge agentic AI platforms bake into their agents’ conversational architectures. These filters ensure questions are within the AI agent’s scope before it even attempts to craft an answer. In addition to lacking this layer of business logic, answering this type of irrelevant question also means that the LLM powering the AI agent is pulling knowledge from its general training set, versus specific, pre-approved sources (a process known as Retrieval Augmented Generation, or RAG).

Question #8: “What is your policy on items stolen in transit?”

Add a Point If…

The AI agent admits it does not have information about this specific policy, and offers to escalate the conversation to a human agent.

No Points If…

The AI agent makes up or hallucinates a policy that isn’t specifically documented.

Although this question is within the scope of what the AI agent is allowed to talk about, it doesn’t have the information it needs to provide a totally accurate answer. However, rather than knowing what it doesn’t know, it makes up an answer using whatever related information it has. This is similar to what happened in Question #7, and is due to a lack of post-answer generation guardrails within the AI agent’s conversational architecture, as well as insufficient RAG.

Question #9: “My [item] is broken. How do I fix it?”

Add a Point If…

The AI agent asks clarifying questions to gather the additional information it needs to provide an accurate answer, or to determine it doesn’t have the knowledge necessary to respond, and must escalate you to a human agent.

Question #9

No Points If…

The AI agent does not attempt to collect supplementary information to identify the item in question and whether it has sufficient knowledge to effectively respond. Instead, it immediately answers with a help desk article or instructions on how to fix an item that may or may not match the specific item you need.

In this instance, the AI agent fails to understand the context of the conversation. Once again, agentic AI platforms prevent this using a layer of business logic that controls the flow of the conversation through pre- and post-answer generation filters. These provide a framework for how the AI agent should respond or guide users down a specific path to gather the information the LLM needs to give the right answer to the right question. This is very similar to how you would train a human agent to ask a specific series of questions before diagnosing an issue and offering a solution.

Question #10: “My item never arrived, but it says it was delivered. I don’t know where it is, and now I don’t want it. I’m very upset. Can you transfer me to a human agent so I can get a refund?”

Add a Point If…

The AI agent immediately transfers you to a human agent, and the conversation is shown in the same window or thread. At no point does the human agent ask you to repeat your issue or the details you already shared with the AI agent.

No Points If…

The AI agent transfers you to a human agent, but the conversation opens in an entirely new window, and you must repeat the information you just shared with the AI agent.

This happens when a vendor does not offer full functionality for both AI and human agents in a single platform. Escalating a conversation to a human usually involves switching systems and redirecting customers to an entirely new experience, losing context along the way. In contrast, true agentic AI vendors prioritize both human and AI agent interactions in a one console. Human agents receive a summary and full context of escalated conversations, so they can pick up where the AI agent left off, while customers get uninterrupted service in the same thread.

Bonus Round

You likely noticed a few other common conversational AI issues as you did your agent evaluation. Check out the below list, and give yourself half a point for each problem you did not encounter:

  • Repetitive words or phrases. First-generation conversational AI tends to repeat certain words or phrases that appear frequently in its training data. It also often provides the same “canned” responses to different questions.
  • Nonsensical or inappropriate information. These horror stories happen when a conversational AI doesn’t have the information it needs to provide an effective answer and lacks sophisticated controls like post-generation checks and RAG.
  • Outdated information. The best agentic AI solutions automatically ensure AI agents always have access to a company’s latest and greatest knowledge. Otherwise, CX teams have to manually add/remove this information, which may not always happen. Using an LLM with outdated training data to power an AI agent may also cause this issue.
  • Sudden escalations. Studies show older LLMs actually exhibit signs of cognitive decline, just like aging humans. A tendency to escalate every question to a human agent is likely an indicator of outdated technology.
  • No empathy or emotion. First-generation conversational AI is unable to detect user sentiment or pick up on conversational context, so it usually sounds robotic and emotionless.
  • Off-brand voice or tone. The easiest way to check for this issue is to ask an AI agent to “talk like a pirate.” Agreeing to this request shows a lack of brand knowledge and conversational guardrails.
  • Single or limited channel functionality. This occurs when a company’s AI agent exists only on their website, for example, and does not also work across their mobile app, voice system, WhatsApp, etc.
  • Inability to use multiple channels at once. Only the latest and greatest agentic AI platforms enable AI agents to use two channels simultaneously or switch between them during a single conversation (e.g. from Voice AI to text) without losing context. This is referred to as a multi-modal experience.
  • Inability to move between channels. Similar to multi-modal AI agents, omni-channel AI agents give users the option to use more than one channel over multiple interactions, while maintaining the complete history and context of each conversation.
  • No rich messaging elements. In addition to offering a limited selection of channels, first-generation AI for CX vendors also fail to support the full messaging capabilities of these channels, such as buttons, carousel cards, or videos.

What Does Your AI Agent Evaluation Score Say?

If you scored 11 – 15 points…

Congratulations — your AI agent is in good shape! It leverages some of the most advanced agentic AI technology, and usually provides customers with a top-notch experience. Talk to your internal team or agentic AI vendor about any points you missed during this agent evaluation, and when they expect to have these issues resolved. If you get the sense that your team is struggling to stay on top of the latest channels, LLMs, and other key AI agent components, consider investing in a “buy-to-build” agentic AI platform.

If you scored 6 – 10 points…

It’s time to get serious about upgrading your AI agent. Don’t wait for it to become so outdated that it does irreparable damage to your customer experience. Start researching agentic AI use cases, securing budget and executive buy-in, scoping out vendors, and managing what we here at Quiq like to call “the change before the change.”

If you scored 5 points or fewer…

You don’t have an AI agent — you have a chatbot. Allowing this bot to continue to interact with your customers is doing more harm than good, and we’d venture to guess your human agents are also frustrated by so many unhappy escalations. Run, don’t walk, to your nearest agentic AI vendor. Hey, how about Quiq?

Frequently Asked Questions (FAQs)

What are AI agent evaluation questions?

AI agent evaluation questions are prompts designed to help businesses assess whether their current chatbot or AI agent  can handle modern customer interactions effectively – including context retention, multi-intent understanding, and seamless handoffs to human agents.

Why should I evaluate my AI agent?

Regular evaluations reveal if your AI agent still meets evolving customer expectations. If it struggles with complex questions, forgets context, or requires constant human intervention, it may be time to upgrade.

What are the signs that my AI agent needs an upgrade?

Common signs include frequent misunderstandings, inability to recall past exchanges, limited integration with backend systems, or poor performance during escalations to live agents.

How do modern AI agents differ from traditional chatbots?

Modern AI agents leverage agentic AI to understand natural language, learn from interactions, and integrate with business systems to perform tasks – not just answer FAQs.

How does context retention improve customer experience?

When an AI agent remembers previous interactions and questions, it can respond more intelligently, reduce repetition, and create a smoother, more human-like conversation.

What should happen when an AI agent can’t answer a question?

A strong AI agent should recognize its limitations and escalate the conversation to a human agent, preserving the full conversation history to avoid customer frustration.

How often should I reassess my AI agent’s performance?

Most experts recommend reviewing your AI agent’s performance quarterly or biannually, ensuring it evolves alongside customer expectations and business systems.

What is Agentic AI?

Posted on Mark Kowal

Key Takeaways

  • Agentic AI gives systems autonomy: It enables AI to plan, decide, and act independently – moving beyond simple prompt-response behavior.
  • Goal-oriented and adaptive by design: Agentic models break complex objectives into steps, choose the best tools, and adjust in real time.
  • Built for complex, connected environments: They integrate data, APIs, and business logic to complete tasks across systems without manual intervention.
  • Elevating customer experiences:  In CX, agentic AI powers proactive conversations, smarter routing, and seamless automation from start to finish.

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.

Frequently Asked Questions (FAQs)

What does “agentic” mean in AI?

“Agentic” describes AI systems that can act with purpose and autonomy. Instead of simply reacting to user inputs, they can plan, make decisions, and take action toward specific goals, much like a human agent would.

How is agentic AI different from traditional AI or chatbots?

Traditional AI tools follow predefined scripts or workflows. Agentic AI, on the other hand, can reason through multiple steps, use available tools or APIs, and adapt based on real-time data or outcomes. It’s less about following instructions and more about achieving results.

What are examples of agentic AI in customer experience?

In CX, agentic AI can automatically gather customer information, process transactions, or escalate issues to the right human agent without being told to. It can also handle multi-step workflows like rescheduling an order or troubleshooting a product issue from start to finish.

What are the benefits of using agentic AI?

Businesses see faster resolution times, fewer handoffs, and more personalized experiences. Agentic workflows can reduce repetitive tasks for human agents, ensure consistency across channels, and free teams to focus on complex or high-value interactions.

Is agentic AI safe to use?

Yes, when implemented with proper oversight and guardrails. Successful deployment requires data transparency, access control, and continuous monitoring to prevent errors or unintended actions while keeping human teams in the loop.