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Month: May 2025

What is Cognitive Architecture? How Intelligent Agents Think, Learn, and Adapt

Posted on J.R. Rettenmeyer

Key Takeaways

  • Cognitive architecture enables human-like AI. It allows intelligent agents to think, learn, and adapt dynamically instead of following rigid, rules-based scripts.
  • Memory, reasoning, and learning work together. These interconnected modules help AI understand context, draw from experience, and improve over time.
  • Rooted in decades of research. Modern systems build on foundational models like ACT-R, Soar, and CLARION to create more cohesive, adaptive intelligence.
  • Transforming customer experiences. Cognitive-driven AI delivers faster resolutions, fewer escalations, and more natural, personalized interactions.

Cognitive architecture is one approach within AI that is changing how we design and implement intelligent systems. Rather than relying on rigid, rules-based scripts, today’s most effective AI systems are designed to think, learn, and act more like humans. This shift enables a new class of digital agents—ones capable of understanding context, remembering past interactions, and adapting their behavior in real time.

Cognitive architectures enable agents to move beyond scripted flows and become more context-aware, conversational, and capable. For CX leaders, this means building experiences that feel natural, responsive, and truly intelligent. As noted in Gartner’s 2024 Hype Cycle for Generative AI, cognitive frameworks and agent orchestration play a key role in the future of automation.

In this article, we’ll define cognitive architecture, explain how it works, walk through real-world use cases, and share how Quiq’s AI Studio uses this framework to power exceptional customer experiences.

From ACT-R To Modern Architecture Models

One of the most influential frameworks in cognitive science is ACT-R (Adaptive Control of Thought–Rational), developed by John Anderson. It breaks cognition into modular components, such as declarative memory and goal management, that work together to simulate thought. Modern cognitive architectures continue to build on these foundational theories to support more dynamic, responsive AI.

The Brain’s Influence On Machine Cognition

Much like the brain’s structure enables humans to think, adapt, and respond, AI systems benefit from conceptual and functional similarities. By modeling decision-making and perception after neural processes, developers build agents that reason in context, make informed choices, and recall relevant past interactions.

The Role of Human Cognitive Architecture

Human cognitive architecture is the theoretical model that describes how the mind receives, stores, and retrieves information. AI systems often emulate these human cognitive functions, drawing on decades of psychological and neuroscience research to build agents with robust memory, learning, and attention capabilities.

Defining Cognitive Architecture

Understanding Cognitive Architecture in CX

Cognitive architecture is what gives AI agents the ability to make sense of information, assess context, and respond and act thoughtfully. Inspired by the way humans think and learn, these systems simulate aspects of intelligence designed for specific domains and problem sets, enabling AI to move from scripted reactions to more adaptive, context-aware decisions.

In customer experience, the ability to respond to customers with relevance and empathy isn’t just a nice-to-have; it’s essential. Cognitive architecture enables AI agents to remember previous conversations, understand customer needs, and tailor their responses based on the situation at hand. This means shorter resolution times, fewer escalations, and more meaningful digital experiences. When a system knows what was said five minutes ago—or five months ago—it stops being a bot and starts acting like a true assistant.

A cognitive architecture is a conceptual and computational model that represents how intelligence works, both in the human mind and in machines. It combines theories from psychology, neuroscience, and computer science to design AI agents that can perceive, decide, and act in real-time.

Unlike narrow AI models that rely on static rule sets or single-task functions, cognitive architectures simulate intelligence by combining capabilities such as memory, learning, reasoning, and goal-directed behavior. These modules work together continuously, enabling systems to adapt dynamically to user needs and context.

This cognitive view influences how developers organize and connect core functions like perception, reasoning, and memory.  Instead of treating these components as isolated modules, cognitive systems are built to mimic how the brain dynamically integrates information, allowing agents to respond fluidly to complex, real-world scenarios.

Cognitive architectures also serve as a bridge between how people think and how machines operate, helping AI systems process information more like humans. Their design makes it possible for digital agents to recognize user intent, apply contextual memory, and respond in ways that feel natural and useful.

As noted in Forbes, contextualization is key to unlocking generative AI’s full potential. When AI systems can understand and act on specific user context, rather than relying on generic responses, they become exponentially more useful in high-touch scenarios like customer service.

“Cognitive architectures are essential for building AI systems that can reason, learn, and adapt across tasks, emulating human intelligence in a way that traditional models cannot.” – Gene Alvarez, Gartner Distinguished VP Analyst

Historical Evolution and Theoretical Foundations

The roots of cognitive architecture can be traced back to the development of systems like ACT-R and Soar. These frameworks provided early models for human cognition and paved the way for computational systems that mimic attention, working memory, and procedural learning.

These frameworks were foundational in shifting AI design from simple, rule-bound models to flexible systems capable of real-time adaptation. The timeline spans from early cognitive models in the 1970s, through the emergence of ACT-R and Soar in the 1990s, to today’s modular, multi-agent platforms that power real-world customer experience solutions.

Types Of Cognitive Architectures

Several well-established cognitive architectures have laid the foundation for how today’s intelligent systems perceive, reason, and learn:

  • ACT-R: This architecture emphasizes modular memory and production rules. It remains influential in modeling how people solve problems and retain knowledge, and its concepts continue to inform AI systems with modular thinking.
  • Soar: Known for its focus on problem-space search and experience-based learning, Soar introduced techniques such as chunking that have shaped reinforcement learning and adaptive systems.
  • CLARION: This hybrid model combines implicit and explicit learning processes, making it especially useful for simulating both intuition and deliberate reasoning-critical for AI that interacts conversationally and emotionally.
  • Sigma: A newer architecture that integrates multiple cognitive functions using graphical models, Sigma aims to unify perception, learning, and decision-making within a single structure-pointing the way toward more holistic and explainable AI systems.

These frameworks vary in complexity and implementation, but each has helped shape the evolution of real-time, reasoning-based AI. Today, cognitive architectures are being integrated with advanced technologies such as large language models (LLMs), generative AI, multimodal inputs, and real-time data orchestration tools. This integration enables agents to combine structured reasoning with deep learning, offering both adaptability and transparency.

As emerging technologies continue to evolve, cognitive architectures provide the backbone for scalable, human-like intelligence-making it possible to blend automation with empathy and insight.

Core Components and Design Principles

Diagram showing how memory, learning, and reasoning interact to support AI decision-making
This visual illustrates how memory, learning, decision-making, coordination, and context feed into a cognitive architecture, enabling AI agents to take informed action.

Breakdown of Essential Components

Modern cognitive architectures typically include several interdependent modules that mirror how humans process and respond to information:

Memory Systems

Agents built on cognitive architecture leverage multiple memory systems—short-term, long-term, and episodic. These systems store information about ongoing conversations, past interactions, and learned behaviors. This memory enables continuity and context awareness.

Decision-Making and Reasoning

Cognitive agents use probabilistic reasoning or symbolic logic to select actions that align with goals and user needs. Unlike static flows, this dynamic reasoning process allows agents to change course based on new information.

Learning Mechanisms

Supervised learning is a core function of cognitive architecture. Agents can adjust their strategies based on feedback, outcomes, or user input. This makes the experience more efficient over time, an essential quality for enterprise automation.

Coordination and Context

In a messaging-first world, understanding the conversational turn and flow matters. Quiq’s cognitive framework builds in response obligations so the agent knows when to follow up. If a user pauses, the agent may nudge: ‘Still there?’ or ‘Need more time?’—just like a human would.

Visual flow of AI response logic when a user pauses during a chat
Quiq’s cognitive architecture includes built-in logic to re-engage customers when conversations go quiet.

Integration in System Design

What makes cognitive architecture uniquely powerful is how these components are integrated into a cohesive system. Rather than existing as standalone modules, memory, reasoning, and learning are designed to interact continuously, enabling agentic behavior that evolves in response to user needs.

In enterprise CX platforms like Quiq, this integration allows agents to maintain continuity across both long conversations and various business messaging channels, pivot naturally when goals shift, and draw from previous interactions to personalize the experience in real time.

By emulating the fluid interplay between memory, intention, and logic that defines human cognition, cognitive architecture lays the foundation for AI that is not only automated but also intelligent.

Applications in AI, Business, and Beyond

Side-by-side illustration comparing static menu-based AI to conversational adaptive agents
A side-by-side comparison of static menu-based AI versus an adaptive, conversational agent powered by cognitive architecture.

Real-World Implementations

Consider a leading home goods retailer using AI to reschedule deliveries. Previously, the experience was rigid: ‘Here are 15 available dates. Pick one.’ Now, the agent asks, ‘Does May 21st work?’ If not, it offers May 22nd. This slight shift in language leads to significantly higher satisfaction scores and reduces abandonment.

Brinks Home Security is a real-world example of this in action. By deploying Quiq’s AI-powered messaging platform, Brinks enabled its customers to interact more naturally when rescheduling installations or service appointments. The system uses memory and intent recognition to propose available time slots and confirm changes, mirroring human-like interaction while improving speed and reducing friction. This illustrates how cognitive architecture can enhance both user satisfaction and operational efficiency.

Brinks Home Security “converted one in 10 of its inbound phone-based customer contacts to use a digital messaging platform over five months.”
– Eileen Brown, ZDNet Contributor

Case Studies and Business Impact

These implementations demonstrate the business value of cognitive architectures, including faster resolution times, fewer escalations, and a customer experience that mirrors natural human interaction. As companies scale, these systems preserve quality and empathy, traits that often get lost (or never existed) in traditional automation.

By enabling agents to reason through user intent, apply contextual understanding, and personalize their responses in real time, businesses can significantly improve KPIs like first contact resolution, average handle time, and CSAT. According to Quiq’s Benchmarking Framework, these improvements are more than theoretical. Quiq customers like Molekule saw a 42% lift in CSAT after integrating AI-driven automation that tailored responses and simplified issue resolution.

Learn more about how Quiq helps leading brands integrate adaptive AI into their customer journeys.

Integration with Modern Technologies

Cognitive architecture also pairs well with the latest AI technologies, including LLMs and multimodal models. When used in orchestration platforms like Quiq’s AI Studio, it allows real-time adaptation across channels (voice, messaging, etc.) and surfaces relevant memory across sessions and touchpoints.

In a world where customers expect immediacy and authenticity, cognitive architecture enables AI to meet those demands, seamlessly and at scale.

What The Analysts Are Saying

Industry analysts increasingly recognize that for enterprise AI to move beyond basic automation into truly human-centered engagement, it must possess traits like context management, adaptive reasoning, and intelligent orchestration. These are precisely the advanced capabilities that cognitive architecture is designed to provide.

Gartner’s 2024 Hype Cycle for Generative AI highlights cognitive AI capabilities, such as context management, adaptive reasoning, and intelligent orchestration, as crucial to transforming customer service from reactive to proactive interactions.

Everest Group makes the case that effective AI should extend beyond efficiency by supporting trust, empathy, and emotional engagement. In their view, long-term customer loyalty stems from blending innovation with a genuine human touch, where technology enhances connection rather than replacing it.

By embedding cognitive architecture into the design of AI systems, companies can enable personalization at scale while delivering the authenticity that customers crave, resulting in smarter, more trusted experiences.

“AI adoption leads to a 35% cost reduction in customer service operations and a 32% revenue increase.”
– Plivo, 2024 AI Customer Service Statistics

Bringing It All Together

Cognitive architecture bridges the gap between technical AI models and real-world human interactions. It provides the adaptability, memory, and reasoning needed to turn automation into assistance and assistance into satisfaction.

“A cognitive architecture gives your AI the adaptability to deliver on any and all customer needs.”

Quiq’s AI Studio brings these principles to life—merging intelligent messaging, contextual memory, and orchestration into a single CX platform that allows agents to adapt, respond, act, and resolve faster than ever before.

For CX leaders looking to future-proof their strategy, investing in cognitive architecture means creating digital agents that grow more capable over time. It ensures that AI doesn’t just perform tasks—it understands them. And in a world where every customer moment counts, that understanding is a powerful differentiator.

Explore More

Frequently Asked Questions (FAQs)

What is cognitive architecture in simple terms?

Cognitive architecture is the framework that defines how an intelligent system thinks, learns, and makes decisions – similar to how the human brain processes information.

How is cognitive architecture different from traditional AI?

Traditional AI follows rules or scripts, while cognitive architecture enables systems to reason, remember, and adapt dynamically based on context and experience.

What are the key components of a cognitive architecture?

Most include memory, learning, perception, and decision-making modules that work together to enable adaptive, human-like behavior.

Why does cognitive architecture matter for customer experience (CX)?

It allows AI agents to understand customers better, recall past interactions, and respond more naturally – leading to faster, more personalized support.

Are there real-world examples of cognitive architecture in action?

Frameworks like ACT-R, Soar, and CLARION have influenced today’s AI systems, powering adaptive assistants and intelligent automation in CX and beyond.

Cognitive Teamwork: The AI and Human Intelligence Strategy That’s Changing Customer Experience

Posted on Greg Dreyfus

As AI evolves, particularly with the arrival of agentic AI, the synergy between AI and human intelligence becomes a key focus for leaders looking to refine and elevate customer experiences (CX). Agentic AI is not about replacing human intelligence, it’s about augmenting it.

With highlights from our most recent guide, this article explores how AI and human intelligence can work together to solve complex problems, build stronger customer trust, and ultimately create exceptional customer experiences.

What is Agentic AI, and How Does It Impact Customer Experiences?

Agentic AI is a form of artificial intelligence designed to autonomously perform tasks, make decisions, and adapt to changing circumstances in real time. Unlike earlier forms of AI that followed strict pre-programmed instructions, agentic AI can analyze situations, reason strategically, and act proactively.

But what does this mean for CX teams?

Simply put, agentic AI is a breakthrough because it shifts the role of AI from a basic task engine to a strategic partner. With this shift, organizations can move beyond cost-cutting to prioritize collaboration, growth, and deeper customer engagement.

Dr. Rameez Kureshi, Lecturer and Director for the University of Hull’s AI Master’s program, summarizes this idea well by saying, “Rather than a zero-sum scenario where AI replaces jobs, the shift will be toward AI-human collaboration, where businesses prioritize efficiency, growth, and customer satisfaction over cost-cutting alone.”

For CX leaders, the challenge lies in navigating this revolution to identify opportunities where AI and human intelligence can complement one another. This guide highlights key areas where this collaboration leads to breakthroughs in CX. For a full deep dive, download our recent guide here.

Four Key Areas to Elevate CX with AI and Human Intelligence

1. Solving Complex Problems Strategically

The Opportunity

AI agents are built to follow a brand’s rules, protocols, and processes—and unlike humans, it won’t break from those. AI is black and white, but the real world often requires a bit of gray, which is where human intelligence comes into play. For example, this gray area is when a Tier 1 human agent would have to get a supervisor’s permission to break protocol. If the customer was talking to AI or a Tier 1 human agent, they would be escalated.

Said another way, while AI excels at handling structured, routine tasks, it struggles with ambiguity and creative problem-solving. This is where human intelligence remains indispensable. Helping customers with complex, nuanced issues often requires subjective judgment, empathy, and out-of-the-box thinking that only humans can provide.

Skills for Human Agents

To succeed in this domain, human agents need to sharpen the following skills:

  • Creative Problem-Solving: Crafting innovative, non-traditional solutions to exceed customer expectations.
  • Critical Thinking: Going beyond surface-level data to analyze, question, and solve customer queries effectively.
  • Domain Expertise: Leveraging in-depth knowledge to anticipate challenges and provide highly educated solutions.
  • Communication: Articulating information clearly while adapting to various customer communication styles.

How Agentic AI Helps

Agentic AI enables human agents to focus on higher-value customer interactions by automating repetitive tasks. Unlike traditional chatbots, which rely on predefined rules, agentic AI leverages large language models (LLMs) to understand and process nuanced customer questions. For example:

  • It can interpret multi-part queries accurately.
  • It integrates with tools like CRMs, booking platforms, and product catalogs to provide human agents with immediate access to actionable insights.

Case Study: Wellness company BODi® leveraged agentic AI to enhance its customer service. By integrating AI agents capable of understanding free-form questions, only 12% of inquiries were escalated to human agents, allowing them to focus on more complex scenarios, while achieving an astounding 88% containment rate. Read the case study >

2. Building Brand Loyalty and Trust

The Opportunity

Modern consumers value businesses that align with their personal values. Achieving emotional connections, authenticity, and trust is critical for brands. AI, however, lacks genuine empathy and emotional intelligence, making human input essential in creating experiences that resonate personally with customers.

“Much like ATMs shifted the role of bank tellers, agentic AI absorbs routine tasks, freeing human agents not for replacement, but for more complex, empathy-driven engagement where their value is truly irreplaceable.” —Greg Dreyfus, Head of Solution Consulting at Quiq

Skills for Human Agents

To build loyalty, CX teams must develop skills in:

  • Brand Storytelling: Weaving company values into all customer interactions.
  • Cultural Sensitivity: Offering inclusive and thoughtful customer experiences.
  • De-escalation: Handling emotional interactions effectively.
  • Emotional Intelligence: Managing and responding empathetically to customers’ emotions.

How Agentic AI Helps

Cutting-edge AI platforms enhance human capabilities, rather than aiming to replicate them. For example:

  • Advanced platforms use pre-answer and post-answer checks to ensure responses are accurate, sensitive, and brand-aligned.
  • Retrieval Augmented Generation (RAG) ensures AI agents only source information from pre-approved databases, protecting customer trust and brand reputation.
  • At the orchestration layer, platforms like Quiq combine LLM logic and business rules to identify each user inquiry and select the right “guide” — or set of instructions, best practices, and tools — for the right use case. This includes routing customers to the appropriate human agent — such as an internal sales rep in the event of a high-value up-sell opportunity — and facilitating channel switching for a more convenient, omni-channel experience — like offering to send delivery updates via SMS versus web chat. In addition, a series of pre- and post-answer generation checks help manage the flow of every conversation to ensure the AI agent stays on-topic (to prevent hallucinations), responds on-brand, provides quality issue resolution, and more.

Example: AI agents can analyze tone during interactions and alert human agents to emotionally charged conversations, ensuring sensitive issues are seamlessly escalated.

3. Driving Top-Line Revenue

The Opportunity

Customer service teams are no longer just cost centers; AI allows them to become revenue drivers. By automating routine inquiries, agentic AI enables human agents to focus on relationship-building and upselling opportunities.

Skills for Human Agents

Key competencies include:

  • Active Listening: Picking up emotional cues and uncovering customer needs.
  • Persuasion: Effectively communicating the benefits of products or services.
  • Agility: Adjusting recommendations in real time to meet evolving customer interests.

How Agentic AI Helps

AI tools empower agents with real-time data and insights that drive personalized sales recommendations:

  • AI agents automatically recognize customer purchase history when integrated with a company’s CRM, surfacing opportunities to upsell based on past purchases and what’s in their shopping cart.
  • Agent-facing AI assistants identify opportunities to offer discounts or recommend complementary products.

Case Study: A leading office supply retailer used agentic AI to enable store associates to provide fast, accurate answers, as well as highlight promotions and add-ons to boost sales. The assistant also sent real-time prompts about upcoming deals, increasing top-line revenue while maintaining customer satisfaction. Read full case study >

4. Managing and Optimizing AI Systems

The Opportunity

AI systems don’t manage themselves. Designing, monitoring, and improving AI requires human oversight to ensure systems achieve business goals responsibly and effectively. This creates new, highly skilled roles, such as AI engineers and AI ethics managers.

Skills for Team Members Interested in Leadership

To excel in management roles, agents must develop:

  • AI Ethics: Ensuring fairness, transparency, and inclusivity in AI actions.
  • Data Literacy: Analyzing and interpreting data to optimize AI systems.
  • Conversational Design: Refining AI interactions for a more human and brand-consistent feel.

How Agentic AI Helps

No-code and pro-code agentic AI platforms enable CX leaders and technical teams to collaborate in designing AI systems that are both efficient and aligned with organizational goals. This collaborative approach optimizes outputs while maintaining key human oversight.

From Threat to Partner: The Value of Collaboration

“AI, particularly agentic AI, transforms work rather than eliminates it. The most successful applications augment human intelligence, enabling employees to focus on meaningful work.” —Dr. Kureshi

AI’s role in CX is clear. It automates repetitive tasks, delivers real-time insights, and frees human teams to excel in areas where they provide the greatest value. With the right AI tools, companies can create a well-rounded, future-proof CX strategy that incorporates the strengths of both AI and human intelligence.

Contact Center Best Practices and Metrics to Track

Posted on Michael Hartsog

No, the call center isn’t dead.

In the last few years, digital channels have seen tremendous growth. People can go online to find answers, send a text, chat with an AI agent, or even reach out on social media.

But while the industry’s focus has shifted to text-based communications, call centers aren’t going anywhere.

Since customers are still dialing, we’ve put together some best practices to help call center agents shine.

Show Customers You Care

Providing great customer service starts and ends with emotions. Answering customers’ questions is vital, but you’re really there to connect with them. And it starts with communicating effectively.

Listen

Listening is the first step. It needs to be said because it sometimes conflicts with other productivity goals. Take the time to listen to a customer’s complaints before diving into a script. Not only will you be better equipped to solve their problems (without a bunch of clarifying questions), but you also give the customer a chance to vent their frustrations and feel heard.

Demonstrate empathy

There’s a big gap between customer expectations and reality. Salesforce reports that 68% of customers expect brands to demonstrate empathy, only 37% of customers feel brands actually do.

Make sure to use phrases like “I understand” or try repeating back what your customer said to show you were listening. These types of responses are especially important over the phone when you can’t rely on visual cues like eye contact and head nodding.

Go off-script

Scripts are great tools to help call center agents solve customer problems, but they can sound stiff and stale. Customers can tell when you’re reading from a script, and it can immediately put a wall up between you.

While it’s helpful to follow the general outline to ensure you don’t miss any important information, inject some of your own personality and mannerisms into it.

Add humor when appropriate, double-back if a customer didn’t give a clear answer, or pull together language from a variety of scenarios.

Customers will appreciate it. (Just make sure it’s within company policy first.)

Avoid Transferring Calls

This one’s tough because it relies on so many other determining factors. But customers have come to expect quick resolutions to their problems, especially when choosing to call customer service over other channels.

In fact, Salesforce reports that 83% of consumers expect to solve their complex problems by speaking with one person.

The truth is, customers don’t want to speak with multiple people to solve their problems. It often means they have to repeat themselves (something customers don’t like), and it increases the time they spend on hold.

The best way to limit transfers?

It often comes down to infrastructure—something that is outside of call center agents’ control. This often includes bigger organizational initiatives like:

  1. Setting up a knowledge base: Information should be easily accessible. That way, when you don’t know the answer, you can pull it up in the knowledge base instead of transferring the call to someone who does know.
  2. Utilizing call center software: There are tons of call center software options that enhance agent and customer experience alike. Some offer options to notify a manager and have them listen to a call to help agents navigate more complex interactions.
  3. Having a system to direct calls: If your company has multiple specialized departments, customer service centers should immediately direct calls to the right person. Call centers can accomplish this using an interactive voice response (IVR) system, web chat, or other self-service tools.
  4. Training agents thoroughly: A knowledge base and customer service software are great tools, but they can’t replace thorough training. Agents should spend time learning the ins and outs of the business, in addition to customer service tactics.

Prepare to Tackle Complex Issues

Call centers aren’t the hub for information anymore. Online communications are growing in popularity.

Between easily accessible information and various other communication channels, making a phone call isn’t the go-to reflex for many customers.

According to Zendesk’s 2020 CX Trends report, 40% of customers choose a channel based on the complexity of their issue. That means when customers have a difficult problem, they’re reaching out to the call center.

Their problem is either too difficult to explain in an email, they’ve tried and failed to search for answers themselves, or they had a bad experience with online customer service in the past. Heck, some people just prefer to talk to someone over the phone. (Yes, they still exist!)

So what does that mean for call center agents? You need to be prepared for anything.

In addition to knowing your products and services inside out, consider conflict resolution training to help upset customers.

Keep these steps in mind when you have to deal with an angry customer:

  1. Stay calm: Easier said than done (we know), but you’ll only escalate the problem if you respond aggressively or defensively. Take deep breaths, and try not to take any of it personally.
  2. Validate your customers’ concerns: Tap into that empathy we talked about earlier. Show you’re listening and actually try to understand the core issue. Most of the time, customers just want to know that they’re talking to someone who can actually fix their problem.
  3. Try not to argue: As much as you want to give the customer all the facts, now’s not the time to correct them. If they’re upset, they aren’t thinking rationally. So, trying to rationalize with them won’t make a difference.
  4. Take responsibility: Check with your company policy on this one first, but it’s generally a good idea to accept responsibility. Apologize when necessary.
  5. Find the solution: Once you’ve figured out the problem, try to find a solution that works within the bounds of your capabilities and satisfies the customer.

Sometimes it’s walking them through a difficult application setup. Other times, it’s offering a replacement product when there’s a failure. Ask call center supervisors for guidance on what’s acceptable to offer a customer to keep them coming back.

Contact Center Metrics to Track

Tracking the right contact center metrics is key to improving both agent performance and customer experience. Without clear benchmarks and data-driven insights, even the most talented teams can fall short of their goals. By focusing on a core set of metrics, you’ll be able to pinpoint areas for improvement, streamline operations, and deliver consistently excellent service.

While every organization has its unique needs, these five foundational contact center performance metrics offer a well-rounded view of how your team is doing and where to optimize. Mastering these metrics will help you drive measurable gains in efficiency, satisfaction, and long-term loyalty.

Average Handle Time (AHT)

Formula: AHT = (Talk Time + Hold Time + After-Call Work Time) / Total Number of Calls

Average Handle Time (AHT) is one of the most widely used contact center metrics. It reflects the total amount of time agents spend handling customer inquiries, from the initial greeting to the final documentation. While lower AHT often signals greater efficiency, it’s important to strike a balance. An AHT that’s too low may indicate rushed interactions that compromise quality.

To optimize AHT without sacrificing customer satisfaction:

  • Build out a robust internal knowledge base so agents can find answers quickly.
  • Equip your team with real-time guidance tools to reduce time spent searching for solutions.
  • Automate repetitive tasks such as call summaries or ticket categorization.

Improving AHT isn’t just about speed, it’s about empowering agents to resolve issues quickly and thoroughly.

First Call Resolution (FCR)

Formula: FCR = (Number of Issues Resolved on First Contact / Total Number of Issues) × 100

First Call Resolution (FCR) measures your team’s ability to resolve customer issues during the first interaction, no callbacks or follow-ups required. High FCR is often associated with increased customer satisfaction, reduced operating costs, and stronger brand loyalty.

Improving this critical contact center performance metric requires a combination of tools, training, and systemic insight:

  • Ensure agents have full access to customer history and context.
  • Train agents to handle complex issues confidently and independently.
  • Analyze repeat calls to uncover and fix recurring problems.

Ultimately, a higher FCR means fewer touchpoints for the customer and a more efficient operation for your team.

Customer Satisfaction (CSAT)

Formula: CSAT = (Number of Satisfied Customers / Total Survey Responses) × 100

Customer Satisfaction (CSAT) is a direct reflection of how your team is perceived. Typically gathered through post-interaction surveys, CSAT gives customers the opportunity to rate their experience, often with a simple question like, “How satisfied were you with your support today?”

To increase CSAT:

  • Send surveys immediately after the interaction while the experience is still fresh.
  • Follow up on negative responses to identify coaching opportunities and process improvements.
  • Focus on empathy and soft skills during training to build a stronger rapport with customers.

CSAT is a leading indicator of your team’s emotional impact on customers and a critical input into broader customer experience strategies.

Service Level

Formula: Service Level = (Calls Answered Within Threshold / Total Calls Answered) × 100

Service level tracks how quickly your team is answering incoming calls. Most contact centers set a goal like answering 80% of calls within 20 seconds. Meeting or exceeding your target helps reduce call abandonment and ensures customers feel heard.

To stay on top of this key contact center metric:

  • Improve forecasting accuracy to anticipate spikes in demand.
  • Adjust staffing in real-time to avoid undercoverage.
  • Use interactive voice response (IVR) systems to route calls more efficiently.

When service levels drop, customer frustration rises. That’s why maintaining high availability is so critical.

Abandonment Rate

Formula: Abandonment Rate = (Abandoned Calls / Total Incoming Calls) × 100

Abandonment rate tells you how many customers hang up before ever reaching a live agent. A high abandonment rate is often a red flag that wait times are too long, or that your system isn’t meeting customers where they are.

To reduce abandonment:

  • Offer virtual hold or scheduled callback options to eliminate long waits.
  • Display estimated wait times so customers know what to expect.
  • Implement better routing or self-service tools to resolve simple issues faster.

Monitoring abandonment closely is essential for understanding both your staffing efficiency and overall contact center performance.

Key Next Steps

Tracking the right contact center metrics lays the groundwork for stronger agent performance and happier customers. But metrics alone aren’t enough. The real impact comes from acting on those insights; adjusting workflows, coaching agents, and evolving your strategy over time.

Set a regular cadence for reviewing performance, and benchmark your results against industry standards and internal goals. Whether you’re focused on voice support or expanding into contact center chat metrics, aligning your KPIs with business outcomes will keep your operation running at peak efficiency.

Call Center Agents are the Frontline

Call centers are still the backbone of the customer service industry. And the most important thing to remember as a call center agent is this: You are your company’s representative.

Follow company policy, but don’t stop there. Put these best practices to use to deliver stellar customer service experiences.

Multimodal LLM: What They Are and How They Work

Posted on Bill O’Neill

Key Takeaways

  • Multimodal LLMs extend beyond text to “see” and “hear.” These models natively process inputs like speech, images, and video in addition to language, enabling a richer, more context-aware understanding compared to traditional text-only LLMs.
  • They unify multiple modalities via a shared architecture. Text, vision, and audio inputs are encoded and then fused in a joint embedding or transformer-based architecture so the model can reason across modalities.
  • The combination of modalities enhances customer experience (CX). In customer service scenarios, a user might speak, send a picture, or upload a video.  A multimodal agent can interpret all simultaneously and respond cohesively, just like a human agent would. 
  • Real-world deployment introduces alignment and infrastructure challenges. Multimodal systems must be able to convert non-text inputs, synchronize and unify text across multiple channels, and handle modality alignment errors.

Artificial Intelligence has entered a new era where language alone is no longer enough. Multimodal LLMs (large language models) are setting the pace for this shift, enabling AI systems to understand and respond to a wide range of human inputs, including text, speech, images, and even video. According to the Gartner, 2024 Hype Cycle for Generative AI, Multimodal GenAI, and open-source LLMs are two transformative technologies with the potential to deliver substantial competitive advantage.

These models are already transforming customer experience (CX), enabling AI agents to process voice input, trigger an SMS, analyze uploaded images, and resolve issues—seamlessly.

In this article, we’ll explain multimodal large language models (MLLMs), how they work, what distinguishes them from traditional LLMs, and how Quiq helps businesses orchestrate them into powerful, real-world customer experiences.

Multimodal CX vs. Multimodal LLMs

While the terms ‘multimodal’ and ‘multichannel’ are often used interchangeably, it’s important to understand their distinctions, especially in CX. A multimodal LLM can natively process different input types (text, images, speech, video). A multimodal CX experience, however, may involve multiple communication channels—such as voice and digital messaging—being used at once. Quiq brings these together, letting users speak on the phone while texting images, with AI understanding and unifying all the inputs in real time, just like a human agent would.

What is a Multimodal LLM?

A multimodal large language model (MLLM) is an AI model that processes diverse types of data, not just written text but also images, speech, and videos. Unlike traditional LLMs that rely solely on language, these models ‘see’ and ‘hear’ to better understand complex contexts. A large language model becomes a tool that can process textual data alongside image or video content to grasp meaning more holistically.

Industry analysts recognize the impact of this shift. Forrester Research identifies multimodal AI as a key trend reshaping automation, particularly in customer experience, where enterprises must meet users across diverse channels and content formats.

Consider an AI-powered customer support agent. A user might describe an issue verbally, send a photo of the product, or even provide a short video. A multimodal LLM integrates all this input to generate intelligent, human-like responses.

This distinction is crucial in CX. A customer might speak to an agent on the phone while simultaneously texting images. The AI interprets both, delivering a coherent experience as a live agent would.

Models like GPT-4V and Gemini are redefining automation capabilities—from identifying garage door models via photos to streamlining flight add-ons over messaging channels.

How Do Multimodal LLMs Work?

To understand how multimodal LLMs work, let’s look at the types of input they handle and how these are processed together:

  • Text: Messages, emails, or voice-to-text transcription
  • Images: Product photos, documents, screenshots
  • Speech: Real-time audio or pre-recorded messages
  • Video: Short clips for product issues or training

Multimodal models rely on a shared architecture—often based on transformers—to encode and unify inputs into a common ‘language’ for reasoning.

Here’s how it typically works:

  • Text Processing interprets language using classic NLP.
  • Vision Modules analyze visual content like product images.
  • Speech Recognition turns audio into structured language.
  • Fusion Layers synthesize inputs to generate relevant, personalized outputs.

Diagram of an LLM AI Agent connected to five functions: Memory, Tools, Action, Multimodal Inputs, and Reflect, illustrating AI orchestration in customer experience.

Picture this: A customer calls to report a stuck recliner. The AI understands the spoken issue and initiates a text message requesting an image of the problem. The customer responds with a photo, and the AI determines whether it’s the correct product, or flags it with something like, ‘Maybe you uploaded the wrong image?’

It’s not just about recognizing the image—it’s about understanding the full context of the conversation and how the image relates to it.

Quiq’s AI Studio orchestrates this complex symphony. It enables enterprises to manage conversations across voice and digital channels, track multimodal inputs in a unified view, and deliver responsive, seamless customer experiences.

From Data Chaos to Clarity: Managing Visual Inputs and Embeddings

A significant challenge in CX is processing image-heavy documentation, like manuals or product guides. Multimodal LLMs enable AI agents to interpret these visual assets by converting them into numerical representations known as embeddings. These allow the AI to understand and retrieve matching visuals or information when customers describe issues via text or upload images. For example, when a customer texts, ‘The blue light is blinking on my garage opener,’ the AI can correlate it to the correct image in a visual troubleshooting guide and offer accurate support instantly.

Top Multimodal Large Language Models

Multimodal LLMs are evolving quickly, and with them, the ways businesses can deliver intelligent automation across customer touchpoints. Each model brings unique strengths: some are designed for high-resolution image analysis, and others excel at parsing spoken conversations or synthesizing insights from video. At Quiq, we believe understanding these capabilities is essential, but what matters even more is having the flexibility to choose the right model for the job. The models below represent some of the most powerful multimodal LLMs in use today.

1. GPT-4V (OpenAI)

  • Combines text and visual understanding
  • Powers complex visual tasks like document parsing or image captioning
  • Enables real-time visual Q&A in AI agents

Use Case: A customer calls about a recliner stuck open. The voice AI processes the inquiry, sends a text requesting an image, and then analyzes the uploaded image to determine if a technician is needed. It sends a follow-up message offering three appointment times to choose from. The result is automation that’s efficient, conversational, and friction-free.

2. Gemini (Google DeepMind)

  • Supports seamless input from text, images, and video
  • Strong at contextual reasoning across formats
  • Deep integration with Google tools

Use Case: A customer calls to add a golf bag to a flight. Instead of sharing credit card info over voice, the AI sends a secure payment link via text, avoiding input errors and improving security.

3. Flamingo (DeepMind)

  • Optimized for image-to-text workflows
  • Learns new tasks with minimal data

Use Case: A customer sends a blurry product image. The model identifies it as the NeuroLift 5500 series and retrieves the right troubleshooting steps—no training is required.

4. LLaVA (Large Language and Vision Assistant)

  • Open-source and ideal for experimentation
  • Supports image + text prompts
  • Great for accessibility and research

Use Case: Customers upload images of issues, such as blinking lights, error messages, and faulty parts. The model interprets and acts on these visuals with precision.

5. Kosmos-1 (Microsoft AI)

  • Excels at vision-language and audio-text integration
  • Ideal for enterprise voice assistants

Use Case: A voice assistant offers to send a rescheduling link via SMS instead of finishing the task by voice. The shift from synchronous to asynchronous reduces user frustration.

LLM-Agnostic by Design: Why Flexibility Matters

While each model offers unique strengths—some excel at image captioning, others at voice-to-text integration—the most powerful CX platforms aren’t defined by the model they use; they’re defined by how well they adapt to what’s needed.

That’s why Quiq is LLM agnostic. Our AI platform isn’t tied to a single provider or model. Instead, we integrate with whichever multimodal LLM best fits the use case, whether that’s GPT-4V for document parsing, Gemini for seamless video-to-text processing, or open-source models like LLaVA for rapid iteration and customization.

This model-agnostic flexibility means:

  • You can select the model that best aligns with your specific CX needs, whether that’s visual accuracy, conversational flow, or speed of response.
  • Your AI architecture stays adaptable as new LLMs and capabilities enter the market.
  • You’re empowered to build around your existing tech ecosystem and regulatory requirements, without compromise.

This LLM-agnostic architecture is at the heart of how we help enterprise teams future-proof their automation strategies while delivering better outcomes for customers and agents alike.

Learn more about our approach to LLM-agnostic AI.

How Quiq Measures AI Agent Success

Quiq employs a variety of performance metrics to assess AI agents:

  • Estimated CSAT: Compares customer satisfaction among human-only, AI-only, and hybrid interactions.
  • Automated Resolution Rate: Evaluates whether the customer’s issue was genuinely resolved, going beyond mere containment.
  • Sentiment Shift: Monitors the emotional tone from the beginning to the end of a conversation.
  • Goal Completion: Measures how often an AI successfully reschedules an appointment, adds a bag, or completes other business-specific outcomes.

These insights help Quiq’s clients continuously refine and improve their AI deployments.

Quiq AI Studio: The Orchestration Layer That Makes It All Work

Multimodal AI agents require orchestration, especially when inputs come from different channels simultaneously. Quiq AI Studio acts as the conductor, tracking and aligning input streams (voice, text, images) in real time, ensuring every message is attributed to the right user session. It supports debugging, prompt testing, and conversation state management. More than a toolkit, it’s a full orchestration layer purpose-built for CX automation.

Learn more about how Quiq’s AI agents enhance customer engagement and streamline support operations.

Future-Proofing with Confidence

LLMs are evolving rapidly—getting faster, cheaper, and smarter. Quiq AI Studio allows businesses to seamlessly upgrade to the latest models using built-in tests, replays, and evaluation tooling. This protects performance while introducing new capabilities like improved visual reasoning, better audio comprehension, or expanded context windows. With Quiq, enterprises can stay ahead of the curve without compromising quality or stability.

What’s Next for Multimodal AI

Looking ahead, multimodal AI will only become more pervasive. LLMs will become true reasoning engines with faster processing, lower cost, and expanded input comprehension. According to Forrester, the rise of predictive and generative AI will transform customer service operations by automating interactions, capturing customer intent, and routing inquiries to appropriately skilled agents. This evolution will allow human agents to focus on complex interactions requiring empathy and personalization, enhancing overall customer satisfaction.

For CX leaders, this opens doors to:

  • Proactive support through predictive multimodal inputs
  • Enhanced personalization from visual cues
  • Smoother handoffs between voice and digital journeys

Yet, as powerful as LLMs are, they are still just tools. The real value lies in how they are orchestrated into real-world customer journeys—removing friction, saving time, and creating brand loyalty through seamless experiences. That’s the promise of multimodal LLMs, brought to life by Quiq.

Explore More

  • Learn how Quiq’s AI Studio makes multimodal AI practical for enterprises.
  • See Customer Success Stories where multimodal models improve automation and satisfaction.
  • Get started with AI Agent Design built for your CX ecosystem.

Frequently Asked Questions (FAQs)

What is a multimodal LLM?

A multimodal LLM is an AI system that can process and understand multiple types of input –  not just text, but also images, audio, and even video. By combining these modalities, it can interpret complex, real-world scenarios in a manner more akin to a human’s.

How is a multimodal LLM different from a traditional LLM?

Traditional LLMs only work with text input and output, while multimodal models can analyze and generate insights across multiple data types. This allows them to respond to visual cues, interpret tone of voice, or describe an image. These are capabilities that text-based models lack.

Why are multimodal LLMs important for customer experience (CX)?

In CX, customers often communicate in multiple ways – sending images, speaking, or typing messages. A multimodal LLM enables seamless understanding across all these inputs, helping brands respond more accurately and naturally while reducing friction for customers.

What are the main challenges in building multimodal systems?

The biggest challenges include aligning different data types into a shared representation, synchronizing context across channels, and maintaining accuracy when one modality is unclear. Ensuring smooth integration across voice, chat, and visual channels also requires robust infrastructure.

How can businesses start using multimodal LLMs?

Companies can integrate multimodal capabilities through APIs or enterprise platforms that support multimodal processing. The key is to start small. For example, by enabling image or voice understanding in customer service, and expanding as infrastructure and data maturity improve.

What’s next for multimodal AI?

Future advancements will focus on improving real-time reasoning, context retention across modalities, and personalization. As models evolve, we’ll see them enabling more natural, human-like digital interactions across industries.