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Engineering Excellence: How to Build Your Own AI Assistant – Part 2

Posted on John Andersen

In Part One of this guide, we explored the foundational architecture needed to build production-ready AI agents – from cognitive design principles to data preparation strategies. Now, we’ll move from theory to implementation, diving deep into the technical components that bring these architectural principles to life when you attempt to build your own AI assistant or agent.

Building on those foundations, we’ll examine the practical challenges of natural language understanding, response generation, and knowledge integration. We’ll also explore the critical role of observability and testing in maintaining reliable AI systems, before concluding with advanced agent behaviors that separate exceptional implementations from basic chatbots.

Whether you’re implementing your first AI assistant or optimizing existing systems, these practical insights will help you create more sophisticated, reliable, and maintainable AI agents.

Section 1: Natural Language Understanding Implementation

With well-prepared data in place, we can focus on one of the most challenging aspects of AI agent development: understanding user intent. While LLMs have impressive language capabilities, translating user input into actionable understanding requires careful implementation of several key components.

While we use terms like ‘natural language understanding’ and ‘intent classification,’ it’s important to note that in the context of LLM-based AI agents, these concepts operate at a much more sophisticated level than in traditional rule-based or pattern-matching systems. Modern LLMs understand language and intent through deep semantic processing, rather than predetermined pathways or simple keyword matching.

Vector Embeddings and Semantic Processing

User intent often lies beneath the surface of their words. Someone asking “Where’s my stuff?” might be inquiring about order status, delivery timeline, or inventory availability. Vector embeddings help bridge this gap by capturing semantic meaning behind queries.

Vector embeddings create a map of meaning rather than matching keywords. This enables your agent to understand that “I need help with my order” and “There’s a problem with my purchase” request the same type of assistance, despite sharing no common keywords.

Disambiguation Strategies

Users often communicate vaguely or assume unspoken context. An effective AI agent needs strategies for handling this ambiguity – sometimes asking clarifying questions, other times making informed assumptions based on available context.

Consider a user asking about “the blue one.” Your agent must assess whether previous conversation provides clear reference, or if multiple blue items require clarification. The key is knowing when to ask questions versus when to proceed with available context. This balance between efficiency and accuracy maintains natural, productive conversations.

Input Processing and Validation

Before formulating responses, your agent must ensure that input is safe and processable. This extends beyond security checks and content filtering to create a foundation for understanding. Your agent needs to recognize entities, identify key phrases, and understand patterns that indicate specific user needs.

Think of this as your agent’s first line of defense and comprehension. Just as a human customer service representative might ask someone to slow down or clarify when they’re speaking too quickly or unclearly, your agent needs mechanisms to ensure it’s working with quality input, which it can properly process.

Intent Classification Architectures

Reliable intent classification requires a sophisticated approach beyond simple categorization. Your architecture must consider both explicit statements and implicit meanings. Context is crucial – the same phrase might indicate different intents depending on its place in conversation or what preceded it.

Multi-intent queries present a particular challenge. Users often bundle multiple requests or questions together, and your architecture needs to recognize and handle these appropriately. The goal isn’t just to identify these separate intents but to process them in a way that maintains a natural conversation flow.

Section 2: Response Generation and Control

Once we’ve properly understood user intent, the next challenge is generating appropriate responses. This is where many AI agents either shine or fall short. While LLMs excel at producing human-like text, ensuring that those responses are accurate, appropriate, and aligned with your business needs requires careful control and validation mechanisms.

Output Quality Control Systems

Creating high-quality responses isn’t just about getting the facts right – it’s about delivering information in a way that’s helpful and appropriate for your users. Think of your quality control system as a series of checkpoints, each ensuring that different aspects of the response meet your standards.

A response can be factually correct, yet fail by not aligning with your brand voice or straying from approved messaging scope. Quality control must evaluate both content and delivery – considering tone, brand alignment, and completeness in addressing user needs.

Hallucination Prevention Strategies

One of the more challenging aspects of working with LLMs is managing their tendency to generate plausible-sounding but incorrect information. Preventing hallucinations requires a multi-faceted approach that starts with proper prompt design and extends through response validation.

Responses must be grounded in verifiable information. This involves linking to source documentation, using retrieval-augmented generation for fact inclusion, or implementing verification steps against reliable sources.

Input and Output Filtering

Filtering acts as your agent’s immune system, protecting both the system and users. Input filtering identifies and handles malicious prompts and sensitive information, while output filtering ensures responses meet security and compliance requirements while maintaining business boundaries.

Implementation of Guardrails

Guardrails aren’t just about preventing problems – they’re about creating a space where your AI agent can operate effectively and confidently. This means establishing clear boundaries for:

  • What types of questions your agent should and shouldn’t answer
  • How to handle requests for sensitive information
  • When to escalate to human agents

Effective guardrails balance flexibility with control, ensuring your agent remains both capable and reliable.

Response Validation Methods

Validation isn’t a single step but a process that runs throughout response generation. We need to verify not just factual accuracy, but also consistency with previous responses, alignment with business rules, and appropriateness for the current context. This often means implementing multiple validation layers that work together to ensure quality responses, all built upon a foundation of reliable information.

Section 3: Knowledge Integration

A truly effective AI agent requires seamlessly integrating your organization’s specific knowledge, layering that on top of the communication capabilities of language models.This integration should be reliable and maintainable, ensuring access to the right information at the right time. While you want to use the LLM for contextualizing responses and natural language interaction, you don’t want to rely on it for domain-specific knowledge – that should come from your verified sources.

Retrieval-Augmented Generation (RAG)

RAG fundamentally changes how AI agents interact with organizational knowledge by enabling dynamic information retrieval. Like a human agent consulting reference materials, your AI can “look up” information in real-time.

The power of RAG lies in its flexibility. As your knowledge base updates, your agent automatically has access to the new information without requiring retraining. This means your agent can stay current with product changes, policy updates, and new procedures simply by updating the underlying knowledge base.

Dynamic Knowledge Updates

Knowledge isn’t static, and your AI agent’s access to information shouldn’t be either. Your knowledge integration pipeline needs to handle continuous updates, ensuring your agent always works with current information.

This might include:

  • Customer profiles (orders, subscription status)
  • Product catalogs (pricing, features, availability)
  • New products, support articles, and seasonal information

Managing these updates requires strong synchronization mechanisms and clear protocols to maintain data consistency without disrupting operations.

Context Window Management

Managing the context window effectively is crucial for maintaining coherent conversations while making efficient use of your knowledge resources. While working memory handles active processing, the context window determines what knowledge base and conversation history information is available to the LLM. Not all information is equally relevant at every moment, and trying to include too much context can be as problematic as having too little.

Success depends on determining relevant context for each interaction. Some queries need recent conversation history, while others benefit from specific product documentation or user history. Proper management ensures your agent accesses the right information at the right time.

Knowledge Attribution and Verification

When your agent provides information, it should be clear where that information came from. This isn’t just about transparency – it’s about building trust and making it easier to maintain and update your knowledge base. Attribution helps track which sources are being used effectively and which might need improvement.

Verification becomes particularly important when dealing with dynamic information. As an AI engineer, you need to ensure that responses are grounded in current, verified sources, giving you confidence in the accuracy of every interaction.

Section 4: Observability and Testing

With the core components of understanding, response generation, and knowledge integration in place, we need to ensure our AI agent performs reliably over time. This requires comprehensive observability and testing capabilities that go beyond traditional software testing approaches.

Building an AI agent isn’t a one-time deployment – it’s an iterative process that requires continuous monitoring and refinement. The probabilistic nature of LLM responses means traditional testing approaches aren’t sufficient. You need comprehensive observability into how your agent is performing, and robust testing mechanisms to ensure reliability.

Regression Testing Implementation

AI agent testing requires a more nuanced approach than traditional regression testing. Instead of exact matches, we must evaluate semantic correctness, tone, and adherence to business rules.

Creating effective regression tests means building a suite of interactions that cover your core use cases while accounting for common variations. These tests should verify not just the final response, but also the entire chain of reasoning and decision-making that led to that response.

Debug-Replay Capabilities

When issues arise – and they will – you need the ability to understand exactly what happened. Debug-replay functions like a flight recorder for AI interactions, logging every decision point, context, and data transformation. This visibility lets you trace paths from input to output, simplifying issue identification and resolution. This level of visibility allows you to trace the exact path from input to output, making it much easier to identify where adjustments are needed and how to implement them effectively.

Performance Monitoring Systems

Monitoring an AI agent requires tracking multiple dimensions of performance. Start with the fundamentals:

  • Response accuracy and appropriateness
  • Processing time and resource usage
  • Business-defined KPIs

Your monitoring system should provide clear visibility into these metrics, allowing you to set baselines, track deviations, and measure the impact of any changes you make to your agent. This data-driven approach focuses optimization efforts on metrics that matter most to business objectives.

Iterative Development Methods

Improving your AI agent is an ongoing process. Each interaction provides valuable data about what’s working and what’s not. You want to establish systematic methods for:

  • Collecting and analyzing interaction data
  • Identifying areas for improvement
  • Testing and validating changes
  • Rolling out updates safely

Success comes from creating tight feedback loops between observation, analysis, and improvement, always guided by real-world performance data.

Section 5: Advanced Agent Behaviors

While basic query-response patterns form the foundation of AI agent interactions, implementing advanced behaviors sets exceptional agents apart. These sophisticated capabilities allow your agent to handle complex scenarios, maintain goal-oriented conversations, and effectively manage uncertainty.

Task Decomposition Strategies

Complex user requests often require breaking down larger tasks into manageable components. Rather than attempting to handle everything in a single step, effective agents need to recognize when to decompose tasks and how to manage their execution.

Consider a user asking to “change my flight and update my hotel reservation.” The agent must handle this as two distinct but related tasks, each with different information needs, systems, and constraints – all while maintaining coherent conversation flow.

Goal-oriented Planning

Outstanding AI agents don’t just respond to queries – they actively work toward completing user objectives. This means maintaining awareness of both immediate tasks and broader goals throughout the conversation.

The agent should track progress, identify potential obstacles, and adjust its approach based on new information or changing circumstances. This might mean proactively asking for additional information when needed or suggesting alternative approaches when the original path isn’t viable.

Multi-step Reasoning Implementation

Some queries require multiple steps of logical reasoning to reach a proper conclusion. Your agent needs to be able to:

  • Break down complex problems into logical steps
  • Maintain reasoning consistency across these steps
  • Draw appropriate conclusions based on available information

Uncertainty Handling

Building on the flexible frameworks established in your initial design, advanced AI agents need sophisticated strategies for managing uncertainty in real-time interactions. This goes beyond simply recognizing unclear requests – it’s about maintaining productive conversations even when perfect answers aren’t possible.

Effective uncertainty handling involves:

  • Confidence assessment: Understanding and communicating the reliability of available information
  • Partial solutions: Providing useful responses even when complete answers aren’t available
  • Strategic escalation: Knowing when and how to involve human operators

The goal isn’t eliminating uncertainty, but to make it manageable and transparent. When definitive answers aren’t possible, agents should communicate limitations while moving conversations forward constructively.

Building Outstanding AI Agents: Bringing It All Together

Creating exceptional AI agents requires careful orchestration of multiple components, from initial planning through advanced behaviors. Success comes from understanding how each component works in concert to create reliable, effective interactions.

Start with clear purpose and scope. Rather than trying to build an agent that does everything, focus on specific objectives and define clear success criteria. This focused approach allows you to build appropriate guardrails and implement effective measurement systems.

Knowledge integration forms the backbone of your agent’s capabilities. While Large Language Models provide powerful communication abilities, your agent’s real value comes from how well it leverages your organization’s specific knowledge through effective retrieval and verification systems.

Building an outstanding AI agent is an iterative process, with comprehensive observability and testing capabilities serving as essential tools for continuous improvement. Remember that your goal isn’t to replace human interaction entirely, but to create an agent that handles appropriate tasks efficiently, while knowing when to escalate to human agents. By focusing on these fundamental principles and implementing them thoughtfully, you can create AI agents that provide real value to your users while maintaining reliability and trust.

Ready to put these principles into practice? Do it with AI Studio, Quiq’s enterprise platform for building sophisticated AI agents.

AI Assistant Builder: An Engineering Guide to Production-Ready Systems – Part 1

Posted on John Andersen

Modern AI agents, powered by Large Language Models (LLMs), are transforming how businesses engage with users through natural, context-aware interactions. This marks a decisive shift away from traditional chatbot building platforms with their rigid decision trees and limited understanding. For AI assistant builders, engineers and conversation designers, this evolution brings both opportunity and challenge. While LLMs have dramatically expanded what’s possible, they’ve also introduced new complexities in development, testing, and deployment.

In Part One of this technical guide, we’ll focus on the foundational principles and architecture needed to build production-ready AI agents. We’ll explore purpose definition, cognitive architecture, model selection, and data preparation. Drawing from real-world experience, we’ll examine key concepts like atomic prompting, disambiguation strategies, and the critical role of observability in managing the inherently probabilistic nature of LLM-based systems.

Rather than treating LLMs as black boxes, we’ll dive deep into the structural elements that make AI agents exceptional – from cognitive architecture design to sophisticated response generation. Our approach balances practical implementation with technical rigor, emphasizing methods that scale effectively and produce consistent results.

Then, in Part Two, we’ll explore implementation details, observability patterns, and advanced features that take your AI agents from functional to exceptional.

Whether you’re looking to build AI assistants for customer service, internal tools, or specialized applications, these principles will help you create more capable, reliable, and maintainable systems. Ready? Let’s get started.

Section 1: Understanding the Purpose and Scope

When you set out to design an AI agent, the first and most crucial step is establishing a clear understanding of its purpose and scope. The probabilistic nature of Large Language Models means we need to be particularly thoughtful about how we define success and measure progress. An agent that works perfectly in testing might struggle with real-world interactions if we haven’t properly defined its boundaries and capabilities.

Defining Clear Objectives

The key to successful AI agent development lies in specificity. Vague objectives like “provide customer support” or “help users find information” leave too much room for interpretation and make it difficult to measure success. Instead, focus on concrete, measurable goals that acknowledge both the capabilities and limitations of your AI agent.

For example, rather than aiming to “answer all customer questions,” a better objective might be to “resolve specific categories of customer inquiries without human intervention.” This provides clear development guidance while establishing appropriate guardrails.

Requirements Analysis and Success Metrics

Success in AI agent development requires careful consideration of both quantitative and qualitative metrics. Response quality encompasses not just accuracy, but also relevance and consistency. An agent might provide factually correct information that fails to address the user’s actual need, or deliver inconsistent responses to similar queries.

Tracking both completion rates and solution paths helps us understand how our agent handles complex interactions. Knowledge attribution is critical – responses must be traceable to verified sources to maintain system trust and accountability.

Designing for Reality

Real-world interactions rarely follow ideal paths. Users are often vague, change topics mid-conversation, or ask questions that fall outside the agent’s scope. Successful AI agents need effective strategies for handling these situations gracefully.

Rather than trying to account for every possible scenario, focus on building flexible response frameworks. Your agent should be able to:

  • Identify requests that need clarification
  • Maintain conversation flow during topic changes
  • Identify and appropriately handle out-of-scope requests
  • Operate within defined security and compliance boundaries

Anticipating these real-world challenges during planning helps build the necessary foundations for handling uncertainty throughout development.

Section 2: Cognitive Architecture Fundamentals

The cognitive architecture of an AI agent defines how it processes information, makes decisions, and maintains state. This fundamental aspect of agent design in AI must handle the complexities of natural language interaction while maintaining consistent, reliable behavior across conversations.

Knowledge Representation Systems

An AI agent needs clear access to its knowledge sources to provide accurate, reliable responses. This means understanding what information is available and how to access it effectively. Your agent should seamlessly navigate reference materials and documentation while accessing real-time data through APIs when needed. The knowledge system must maintain conversation context while operating within defined business rules and constraints.

Memory Management

AI agents require sophisticated memory management to handle both immediate interactions and longer-term context. Working memory serves as the agent’s active workspace, tracking conversation state, immediate goals, and temporary task variables. Think of it like a customer service representative’s notepad during a call – holding important details for the current interaction without getting overwhelmed by unnecessary information.

Beyond immediate conversation needs, agents must also efficiently handle longer-term context through API interactions. This could mean pulling customer data, retrieving order information, or accessing account details. The key is maintaining just enough state to inform current decisions, while keeping the working memory focused and efficient.

Decision-Making Frameworks

Decision making in AI agents should be both systematic and transparent. An effective framework begins with careful input analysis to understand the true intent behind user queries. This understanding combines with context evaluation – assessing both current state and relevant history – to determine the most appropriate action.

Execution monitoring is crucial as decisions are made. Every action should be traceable and adjustable, allowing for continuous improvement based on real-world performance. This transparency enables both debugging when issues arise and systematic enhancement of the agent’s capabilities over time.

Atomic Prompting Architecture

Atomic prompting is fundamental to building reliable AI agents. Rather than creating complex, multi-task prompts, we break down operations into their smallest meaningful units. This approach significantly improves reliability and predictability – single-purpose prompts are more likely to produce consistent results and are easier to validate.

A key advantage of atomic prompting is efficient parallel processing. Instead of sequential task handling, independent prompts can run simultaneously, reducing overall response time. While one prompt classifies an inquiry type, another can extract relevant entities, and a third can assess user emotion. These parallel operations improve efficiency while providing multiple perspectives for better decision-making.

The atomic nature of these prompts makes parallel processing more reliable. Each prompt’s single, well-defined responsibility allows multiple operations without context contamination or conflicting outputs. This approach simplifies testing and validation, providing clear success criteria for each prompt and making it easier to identify and fix issues when they arise.

For example, handling a customer order inquiry might involve separate prompts to:

  • Classify the inquiry type
  • Extract relevant identifiers
  • Determine needed information
  • Format the response appropriately

Each step has a clear, single responsibility, making the system more maintainable and reliable.

When issues do occur, atomic prompting enables precise identification of where things went wrong and provides clear paths for recovery. This granular approach allows graceful degradation when needed, maintaining an optimal user experience even when perfect execution isn’t possible.

Section 3: Model Selection and Optimization

Choosing the right language models for your AI agent is a critical architectural decision that impacts everything from response quality to operational costs. Rather than defaulting to the most powerful (and expensive) model for all tasks, consider a strategic approach to model selection.

Different components of your agent’s cognitive pipeline may require different models. While using the latest, most sophisticated model for everything might seem appealing, it’s rarely the most efficient approach. Balance response quality with resource usage – inference speed and cost per token significantly impact your agent’s practicality and scalability.

Task-specific optimization means matching different models to different pipeline components based on task complexity. This strategic selection creates a more efficient and cost-effective system while maintaining high-quality interactions.

Language models evolve rapidly, with new versions and capabilities frequently emerging. Design your architecture with this evolution in mind, enabling model version flexibility and clear testing protocols for updates. This approach ensures your agent can leverage improvements in the field while maintaining reliable performance.

Model selection is crucial, but models are only as good as their input data. Let’s examine how to prepare and organize your data to maximize your agent’s effectiveness.

Section 4: Data Collection and Preparation

Success with AI agents depends heavily on data quality and organization. While LLMs provide powerful baseline capabilities, your agent’s effectiveness relies on well-structured organizational knowledge. Data organization, though typically one of the most challenging and time-consuming aspects of AI development, can be streamlined with the right tools and approach. This allows you to focus on building exceptional AI experiences rather than getting bogged down in manual processes.

Dataset Curation Best Practices

When preparing data for your AI agent, prioritize quality over quantity. Start by identifying content that directly supports your agent’s objectives – product documentation, support articles, FAQs, and procedural guides. Focus on materials that address common user queries, explain key processes, and outline important policies or limitations.

Data Cleaning and Preprocessing

Raw documentation rarely comes in a format that’s immediately useful for an AI agent. Think of this stage as translation work – you’re taking content written for human consumption and preparing it for effective AI use. Long documents must be chunked while maintaining context, key information extracted from dense text, and formatting standardized.

Information should be presented in direct, unambiguous terms, which could mean rewriting complex technical explanations or breaking down complicated processes into clearer steps. Consistent terminology becomes crucial throughout your knowledge base. During this process, watch for:

  • Outdated information that needs updating
  • Contradictions between different sources
  • Technical details that need validation
  • Coverage gaps in key areas

Automated Data Transformation and Enrichment

Manual data preparation quickly becomes unsustainable as your knowledge base grows. The challenge isn’t just handling large volumes of content – it’s maintaining quality and consistency while keeping information current. This is where automated transformation and enrichment processes become essential.

Effective automation starts with smart content processing. Tools that understand semantic structure can automatically segment documents while preserving context and relationships, eliminating the need for manual chunking decisions.

Enrichment goes beyond basic processing. Modern tools can identify connections between information, generate additional context, and add appropriate classifications. This creates a richer, more interconnected knowledge base for your AI agent.

Perhaps most importantly, automated processes streamline ongoing maintenance. When new content arrives – whether product information, policy changes, or updated procedures – your transformation pipeline processes these updates consistently. This ensures your AI agent works with current, accurate information without constant manual intervention.

Establishing these automated processes early lets your team focus on improving agent behavior and user experience rather than data management. The key is balancing automation with oversight to ensure both efficiency and reliability.

What’s Next?

The foundational elements we’ve covered – from cognitive architecture to knowledge management – are essential building blocks for production-ready AI agents. But understanding architecture is just the beginning.

In Part Two, we’ll move from principles to practice, exploring implementation patterns, observability systems, and advanced features that separate exceptional AI agents from basic chatbots. Whether you’re building customer service assistants, internal tools, or specialized AI applications, these practical insights will help you create more capable, reliable, and sophisticated systems.

Read the next installment of this guide: Engineering Excellence: How to Build Your Own AI Assistant – Part 2

How the AI Chatbot for Customer Service Became the AI Agent (And How It Actually Works)

Posted on Max Fortis

Chatbots have become a staple in customer service for brands across the world. This is why eight out of ten businesses have some kind of chatbot on their website to help customers in their journey.

And it’s not hard to see why, as there are myriad benefits to using such AI support chatbots. They’re available 24/7, without having to take breaks or sick days; they’re able to handle multiple conversations simultaneously; they’re cost efficient and scalable; they can personalize queries to each individual (more on this shortly); and they boost customer satisfaction.

Perhaps this is why the chatbot market was thought to be worth nearly $5 billion in 2022, a figure estimated to triple before the end of this decade.

But having said that, there’s a lot of diversity hidden under the ‘chatbot’ label. There are many techniques for building chatbots, these techniques have changed over time, and today, there are ‘AI agents’ which need to be distinguished from the older chatbots they replaced.

This is what we’re here to discuss today. We’ll first define AI chatbots in the context of customer service, provide an overview of their history, and how they’re different from the agents rapidly changing the contact center industry.

What is an AI Chatbot for Customer Service?

An AI chatbot for customer service refers to a program, platform, or machine-learning model that can perform some fraction of the work done by customer service agents.

Chatbots vary widely in complexity. First, there were the simple rule-based systems of yesteryear that attempted to understand the intent of a query and match it to an appropriate, pre-defined response. Over time, advances in machine learning, natural language processing, and data storage led to the billion-parameter large language models we use now, which can respond flexibly and dynamically under a range of circumstances, even to questions that are ambiguous or contradictory.

These are incredibly different offerings, but we won’t do more than point that out for now, as this will be our focus for most of the rest of the piece.

Regardless, an AI customer service chatbot generally lives on a company’s website, where it can answer questions. It has also become common to integrate them into various communication channels, such as Apple Messages for Business, WhatsApp, Voice, and email.

Though there are critical aspects of human interactions that are still not outsourceable to algorithms, customers have gradually become more willing to talk directly to AI chatbots to resolve their customer service issues. Surveys have shown that almost everyone has heard of chatbots and understands in general terms what they are. And nearly three-quarters of respondents prefer chatbots over humans to quickly get simple questions answered. When asked whether they were satisfied with their last interaction with a chatbot, 69% said ‘yes,’ and a little over half cited long wait times as one of their chief frustrations.

How AI Chatbots Have Evolved

As promised, let’s now discuss some of the ways in which the customer service AI chatbot has changed over time.

Here’s a broad overview, taken from TechTarget:

The Evolution of Chatbots

What are the Kinds of AI Chatbots?

You’ll notice the chart above tracks three broad types of chatbots, which is a categorization we more or less agree with—though we think there’s an important distinction between chatbots and agents, which isn’t reflected here. (We discuss that more in the next section).

The first kind of chatbot to be developed was by far the simplest, and it emerged from research done in the 1960s. These were based on a primitive model known as a ‘decision tree,’ and were only suitable for basic, formulaic interactions where there were clear rules and virtually no room for either ambiguity or creativity. In contact centers, robust AI agents are replacing these, but you might still see them answering the most common questions.

Although there was a lot of research into methods like neural networks, these ‘scripted chatbots’ were more or less the standard for the next four decades, until the field of natural language processing made enough progress to power a different approach.

Once it became possible to use sentiment analysis to detect emotional tones in writing, and entity extraction to automatically detect information like product names and formal titles (to pick just two examples), the road was paved to create more powerful ‘conversational chatbots.’

Unlike their predecessors, conversational chatbots could carry on much longer-range, multi-turn interactions, and help in a much broader variety of circumstances. Common examples of these tools are Siri and Alexa, both of which can process voice commands, look things up, fetch information, and even perform simple tasks (like scheduling a meeting or adding a reminder to your calendar).

Then, we come to the modern crop of AI chatbots, which are so much more powerful and far-reaching it’s better to call them ‘agents’ instead of ‘chatbots.’ These ‘generative AI agents’ are built around large language models, made famous by the release of ChatGPT in November of 2022.

For the most part, AI agents aren’t actually a new kind of technology, as neural networks have been around for a while and have been used in chatbots for a while, too. No, the single biggest distinguishing feature is that the networks are so big and are trained on such a bewildering variety of data that they can do things prior iterations couldn’t do.

No doubt you’ve spent some time playing with these models, or you’ve seen demonstrations of them, and you know what we mean. They can write code and poems, translate near-instantaneously between dozens of languages, describe (and generate) images (and videos), and take on all sorts of subtle postures in their interactions with humans. They can be instructed to act like a kindly grandma, for example, a stern teacher from fifth-grade, or an exceptionally polite and deferential friend.

This is precisely the reason that generative AI is having such a profound impact on contact centers. It can do so many things, and there are so many ways to fine-tune and tweak it, that people are finding dozens of places to use it. It’s not so much replacing human agents as it is dramatically simplifying and accelerating their workflows in hundreds of little ways.

AI Chatbots vs. AI Agents

Okay, now let’s get to the main distinction we want to draw out in this piece, the one between ‘AI chatbots’ and ‘AI agents’. In doing so, we’ll provide CX leaders with the valuable context they need to make the best decisions about the technological tools they deploy and invest in.

First, we’ve already written a lot about chatbots, so let’s define an ‘agent.’ Broadly speaking, an agent is an entity that can take one or more actions in pursuit of an overarching goal. Some agents are very simple, like single-celled organisms that just sort of float around looking for food, while some are very complex, such as the humans working out ways to terraform Mars.

But what they all have in common is a goal.

An AI agent is the same thing, it’s an artificial entity that can usually achieve a goal, like ‘download these data files and create a line chart with them’ or ‘check these six sources on quantum computing and summarize your findings.’

As with chatbots more generally, agents aren’t exactly new. We’ve been working with reinforcement learning agents for years, for example. But generative AI has opened up a whole new frontier, and the agent projects being built on top of it are really exciting.

A full discussion of this frontier is outside the scope of this article, but you can check out our piece on the future of generative AI for a discussion of specific agent projects.

7 Best Practices for Using AI Agents

What does concern us here is the impact this will have on CX leaders and the contact centers they manage, which is why we’ll cover some of the best practices of successfully using AI agents in this section.

1. Be clear about your intended use of AI agents.

As we’ve already mentioned, generative AI is great at many tasks, but the way to get the most out of it is to identify which KPIs you’re trying to drive and what changes you want to see, then implementing an agent that can help get you there. In other words, don’t be overwhelmed by its possibilities; start by drilling down into a few promising use cases and expand as appropriate.

2. Focus on design and access.

You want to be sure that the interface customers use to interact with your chatbot is sleek, intuitive, and easy to find. You can have the most powerful AI agent in the world, but it won’t do you much good if people hate using it or they can’t locate it in the first place.

3. Use full personalization.

One of the reasons modern chatbots are so powerful is that they can use retrieval-augmented generation to ‘ground’ their generations in sources of information–knowledge bases, product feeds, CRMs, Notion pages, etc. This makes replies more useful, while also making your customers feel more heard. So make sure your AI agent has access to the systems or data it needs to take action and be helpful (as you would with a new employee).

4. Gather feedback and improve.

AI agents are capable of being improved in a bevy of different ways. You should implement systems to gather your users’ impressions and use them to update your systems.

5. Let AI and humans play their strongest roles.

AI agents are great at many tasks, but others need a human’s superior flexibility and insight. The key here is to craft a system that can seamlessly switch between humans and agents.

6. Have your AI agents be proactive.

AI agents can be configured to reach out on their own if a user engages in certain behaviors or otherwise seems confused. For example, one well-known furniture brand and Quiq customer implemented Proactive AI and a Product Recommendation engine, which led to the largest sales day in the company’s history through increased chat sales.

7. Ensure transparency.

Most of us are really excited about the promise of generative AI, but one thing that has many concerned is the way data is used by these models, and their broader implications for privacy. Make your policies clear, and make sure you are being responsible with the data your customers trust you with.

You can use these best practices when designing your own AI agent system, but the easier way forward is to treat them as a checklist when you’re shopping around for third-party platforms.

AI Agents and You

Large language models, and the AI agents they make possible, will be a key part of the future of contact centers. If you want to learn more about this technology and the ways to harness it to redefine CX success, check out our latest guide.

Evolving the Voice AI Chatbot: From Bots to Voice AI Agents & Their Impact on CX Leaders

Posted on Mark Kowal

Voice AI has come a long way from its humble beginnings, evolving into a powerful tool that’s reshaping customer service. In this blog, we’ll explore how Voice AI has grown to address its early limitations, delivering impactful changes that CX leaders can no longer ignore. Learn how these advancements create better customer experiences, and why staying informed is essential to staying competitive.

The Voice AI Journey

Customer expectations have evolved rapidly, demanding faster and more personalized service. Over the years, voice interactions have transformed from rigid, rules-based AI chatbot with voice systems to today’s sophisticated AI-driven solutions. For CX leaders, Voice AI has emerged as a crucial tool for driving service quality, streamlining operations, and meeting customer needs more effectively.

Key Concepts

Before diving into this topic, readers, especially CX leaders, should be familiar with the following key terms to better understand the technology and its impact. The following is not a comprehensive list, but should provide the background to clarify terminology and identify the key aspects that have contributed to this evolution.

Speech-enabled systems vs. chatbots vs. AI agents

  • Speech-enabled systems: Speech-enabled systems are basic tools that convert spoken language into text, but do not include advanced features like contextual understanding or decision-making capabilities.
  • Chatbots: Chatbots are systems that interact with users through text, answering questions, and completing tasks using either set rules or AI to understand user inputs.
  • AI agents: AI agents are smart conversational systems that help with complex tasks, learn from interactions, and adjust their responses to offer more personalized and relevant assistance over time.

Rules-based (previous generation) vs. Large Language Models or LLMs (next generation)

  • Previous gen: Lacks adaptability, struggles with natural language nuances, and fails to offer a personalized experience.
  • Next-gen (LLM-based): Uses LLMs to understand intent, generate responses, and evolve based on context, improving accuracy and depth of interaction.

Agent Escalation: A process in which the Voice AI system hands off the conversation to a human agent, often seamlessly.

AI Agent: A software program that autonomously performs tasks, makes decisions, and interacts with users or systems using artificial intelligence. It can learn and adapt over time to improve its performance, commonly used in customer service, automation, and data analysis.

Depending on their purpose, AI agents can be customer-facing or assist human agents by providing intelligent support during interactions. They function based on algorithms, machine learning, and natural language processing to analyze inputs, predict outcomes, and respond in real-time.

Automated Speech Recognition (ASR): The technology that enables machines to understand and process human speech. It’s a core component of Voice AI systems, helping them identify spoken words accurately.

Context Awareness: Voice AI’s ability to remember previous interactions or conversations, allowing it to maintain a flow of dialogue and provide relevant, contextually appropriate responses.

Conversational AI: Conversational AI refers to technologies that allow machines to interact naturally with users through text or speech, using tools like LLMs, NLU, speech recognition, and context awareness.

Conversation Flow: The logical structure of a conversation, including how the Voice AI chatbot guides interactions, asks follow-up questions, and handles different branches of user input.

Generative AI: A type of artificial intelligence that creates new content, such as text, images, audio, or video, by learning patterns from existing data. It uses advanced models, like LLMs, to generate outputs that resemble human-made content. Generative AI is commonly used in creative fields, automation, and problem-solving, producing original results based on the data it has been trained on.

Intent Recognition: The process by which a Voice AI system identifies the user’s goal or purpose behind their speech input. Understanding intent is critical to delivering appropriate and relevant responses.

LLMs: LLMs are sophisticated machine learning systems trained on extensive text data, enabling them to understand context, generate nuanced responses, and adapt to the conversational flow dynamically.

Machine Learning (ML): A type of AI that allows systems to automatically learn and improve from experience without being explicitly programmed. ML helps voice AI chatbots adapt and improve their responses based on user interactions.

Multimodal: The ability of a system or platform to support multiple modes of communication, allowing customers and agents to interact seamlessly across various channels.

Multi-Turn Conversations: This refers to the ability of Voice AI systems to engage in extended dialogues with users across multiple steps. Unlike simple one-question, one-response setups, multi-turn conversations handle complex interactions.

Natural Language Processing (NLP): Consists of a branch of AI that helps computers understand and interpret human language. It is the key technology behind voice and text-based AI interactions.

Omnichannel Experience: A seamless customer experience that integrates multiple channels (such as voice, text, and chat) into one unified system, allowing customers to seamlessly transition between them.

Rules-based approach: This approach uses predefined scripts and decision trees to respond to user inputs. These systems are rigid, with limited conversational abilities, and struggle to handle complex or unexpected interactions, leading to a less flexible and often frustrating user experience.

Sentiment Analysis: A feature of AI that interprets the emotional tone of a user’s input. Sentiment analysis helps Voice AI determine the customer’s mood (e.g., frustrated or satisfied) and tailor responses accordingly.

Speech Recognition / Speech-to-Text (STT): Speech Recognition, or Speech-to-Text (STT), converts spoken language into text, allowing the system to process it. It’s a key step in making voice-based AI interactions possible.

Text-to-Speech (TTS): The opposite of STT, TTS refers to the process of converting text data into spoken language, allowing digital solutions to “speak” responses back to users in natural language.

Voice AI: Voice AI is a technology that uses artificial intelligence to understand and respond to spoken language, allowing machines to have more natural and intuitive conversations with people.

Voice User Interface (VUI): Voice User Interface (VUI) is the system that enables voice-based interactions between users and machines, determining how naturally and effectively users can communicate with Voice AI systems.

The humble beginnings of rules-based voice systems

Voice AI has been nearly 20 years in the making, starting with basic rules-based systems that followed predefined scripts. These early systems could automate simple tasks, but if customers asked anything outside the programmed flow, the system fell short. It couldn’t handle natural language or adapt to the unexpected, leading to frustration for both customers and CX teams.

For CX leaders, these systems posed more challenges than solutions. Robotic interactions often required human intervention, negating the efficiency benefits. It became clear that something more flexible and intelligent was needed to truly transform customer service.

The rise of AI and speech-enabled systems

As businesses encountered the limitations of rules-based systems, the next chapter in the evolution of Voice AI introduced speech-enabled systems. These systems were a step forward, as they allowed customers to interact more naturally with technology by transcribing spoken language into text. However, while they could accurately convert speech to text which solved one issue, they still struggled with a critical challenge—they couldn’t grasp the underlying meaning or the sentiment behind the words.

This gap led to the emergence of the first generation of AI, which represented a significant improvement over simple chatbots. This intelligence improved more helpful for customer interactions, but they still fell short in providing the seamless, human-like conversations that CX leaders envisioned. While customers could speak to AI-powered systems, the experience was often inconsistent, especially when dealing with complex queries. The advancement of AI was another improvement, but it was still limited by the rules-based logic it evolved from.

The challenge stemmed from the inherent complexity of language. People express themselves in diverse ways, using different accents, phrasing, and expressions. Language rarely follows a single, rigid pattern, which made it difficult for early speech systems to interpret accurately.
These AI systems were a huge leap in progress and created hope for CX leaders. Intelligent systems that can adapt and respond to users’ speech were powerful, but not enough to make a full transformation in the CX world.

The AI revolution: From rules-based to next-gen LLMs

The real breakthrough came with the rise of LLMs. Unlike rigid rules-based systems, LLMs use neural networks to understand context and intent, creating true natural, fluid human-like conversations. Now, AI could respond intelligently, adapt to the flow of interaction, and provide accurate answers.

For CX leaders, this was a pivotal moment. No more frustrating dead ends or rigid scripts—Voice AI became a tool that could offer context-aware services, helping businesses cut costs while enhancing customer satisfaction. The ability to deliver meaningful, efficient service marked a turning point in customer engagement.

What makes Voice AI work today?

Today’s Voice AI systems combine several advanced technologies:

  • Speech-to-Text (STT): Converts spoken language into text with high accuracy.
  • AI Intelligence: Powered by NLU and LLMs, the AI deciphers customer intent and delivers contextually relevant responses.
  • Text-to-Speech (TTS): Translates the AI’s output back into natural-sounding speech for smooth and realistic communication.

These technologies work together to enable smarter, faster service, reduce the load on human agents and provide an intuitive customer experience.

The transformation: What changed with next-gen Voice AI?

With advancements in NLP, ML, and omnichannel integration, Voice AI has evolved into a dynamic, intelligent system capable of delivering personalized, empathetic responses. Machine Learning ensures that the system learns from every interaction, continuously improving its performance. Omnichannel integration allows Voice AI to operate seamlessly across multiple platforms, providing a unified customer experience. This is crucial for the transformation of customer service.

Rather than simply enhancing voice interactions, omnichannel solutions select the best communication channel within the same interaction, ensuring customers receive a complete answer and any necessary documentation to resolve their issue – via email or SMS.

For CX leaders, this transformation enables them to offer real-time, personalized service, with fewer human touchpoints and greater customer satisfaction.

The four big benefits of next-gen Voice AI for CX leaders

The rise of next-gen Voice AI from previous-gen Voice AI chatbots offers CX leaders powerful benefits, transforming how they manage customer interactions. These advancements not only enhance the customer experience, but also streamline operations and improve business efficiency.

1. Enhanced customer experience

With faster, more accurate, and context-aware responses, Voice AI can handle complex queries with ease. Customers no longer face frustrating dead ends or robotic answers. Instead, they get intelligent, conversational interactions that leave them feeling heard and understood.

2. 24/7 availability

Voice AI is always on, providing customers with support at any time, day or night. Whether it’s handling routine inquiries or resolving issues, Voice AI ensures customers are never left waiting for help. This around-the-clock service not only boosts customer satisfaction, but also reduces the strain on human agents.

3. Operational efficiency

By automating high volumes of customer interactions, Voice AI significantly reduces human intervention, cutting costs. Agents can focus on more complex tasks, while Voice AI handles repetitive, time-consuming queries—making customer service teams more productive and focused.

4. Personalization at scale

By learning from each interaction, the system can continuously improve and deliver tailored responses to individual customers, offering a more personalized experience for every user. This level of personalization, once achievable only through human agents, is now possible on a much larger scale.
However, while machine learning plays a critical role in making these advancements possible, it is not a “magical” solution. The improvements happen over time, as the system processes more data and refines its understanding. Although this may sound simplified, the gradual and ongoing development of machine learning can indeed lead to highly effective and powerful outcomes in the long run.

The future of Voice AI: Next-gen experience in action

Voice AI’s future is already here, and it’s evolving faster than ever. Today’s systems are almost indistinguishable from human interactions, with conversations flowing naturally and seamlessly. But the leap forward doesn’t stop at just sounding more human—Voice AI is becoming smarter and more intuitive, capable of anticipating customer needs before they even ask. With AI-driven predictions, Voice AI can now suggest solutions, recommend next steps, and provide highly relevant information, all in real time.

Imagine a world where Voice AI understands customer’s speech and then anticipates what is needed next. Whether it’s guiding them through a purchase, solving a complex issue, or offering personalized recommendations, technology is moving toward a future where customer interactions are smooth, proactive, and entirely customer-centric.

For CX leaders, this opens up incredible opportunities to stay ahead of customer expectations. Those adopting next-gen Voice AI now are leading the charge in customer service innovation, offering cutting-edge experiences that set them apart from competitors. And as this technology continues to evolve, it will only get more powerful, more intuitive, and more essential for delivering world-class service.

The new CX frontier with Voice AI

As Voice AI continues to evolve from the simple Voice AI chatbot of yesteryear, we are entering a new frontier in customer experience. What started as a rigid, rules-based system has transformed into a dynamic, intelligent agent capable of revolutionizing how businesses engage with their customers. For CX leaders, this new era means greater personalization, enhanced efficiency, and the ability to meet customers where they are—whether it’s through voice, chat, or other digital channels.

We’ve made more progress in this development, but it is far from over. Voice AI is expanding, from deeper integrations with emerging technologies to more advanced predictive capabilities that can elevate customer experiences to new heights. The future holds more exciting developments, and staying ahead will require ongoing adaptation and willingness to embrace change.

Omnichannel capabilities is just the beginning

One fascinating capability of Voice AI is its ability to seamlessly integrate across multiple platforms, making it a truly omnichannel experience. For example, imagine you’re on a phone call with an AI agent, but due to background noise, it becomes difficult to hear. You could effortlessly switch to texting, and the conversation would pick up exactly where it left off in your text messages, without losing any context.

Similarly, if you’re on a call and need to share a photo, you can text the image to the AI agent, which can interpret the content of the photo and respond to it—all while continuing the voice conversation.

Another example of this multi-modal functionality is when you’re on a call and need to spell out something complex, like your last name. Rather than struggle to spell it verbally, you can simply text your name, and the Voice AI system will incorporate the information without disrupting the flow of the interaction. These types of seamless transitions between different modes of communication (voice, text, images) are what make multi-modal Voice AI truly revolutionary.

Voice AI’s future is already here, and it’s evolving rapidly. Today’s systems are approaching a level where they are almost indistinguishable from human interactions, with conversations flowing naturally and effortlessly. But the advancements go beyond merely sounding human—Voice AI is becoming smarter and more intuitive, capable of anticipating customer needs before they even express them. With AI-driven predictions, these systems can now suggest solutions, recommend next steps, and provide highly relevant information in real-time.

Imagine a scenario where Voice AI not only understands what a customer says, but also predicts what they might need next. Whether it’s guiding them through a purchase, solving a complex problem, or offering personalized product recommendations, this technology is leading the way toward a future where customer interactions are smooth, proactive, and deeply personalized.

For CX leaders, these capabilities open up unprecedented opportunities to exceed customer expectations. Those adopting next-generation Voice AI are positioning themselves at the forefront of customer service innovation, offering cutting-edge experiences that differentiate them from competitors. As this technology continues to advance, it will become even more powerful, more intuitive, and essential for delivering exceptional, customer-centric service.

Voice AI’s exciting road ahead

From the original Voice AI chatbot to today, Voice AI’s evolution has already transformed the customer experience—and the future promises continued innovation. From intelligent human-like conversations to predictive capabilities that anticipate needs, Voice AI is destined to change the way businesses interact with their customers in profound ways.

The exciting thing is that this is just the beginning.

The next wave of Voice AI advancements will open up new possibilities that we can only imagine. As a CX leader, the opportunity to harness this technology and stay ahead of customer expectations is within reach. It could be the most exciting time to be at the forefront of these changes.

At Quiq, we are here to guide you through this journey. If you’re curious about our Voice AI offering, we encourage you to watch our recent webinar on how we harness this incredible technology.

One thing is for sure, though: As the landscape continues to evolve, we’ll be right alongside you, helping you adapt, innovate, and lead in this new era of customer experience. Stay tuned, because the future of Voice AI is just getting started, and we’ll continue to share insights and strategies to ensure you stay ahead in this rapidly changing world.

National Furniture Retailer Reduces Escalations to Human Agents by 33%

Posted on Mike Zinne

A well-known furniture brand faced a significant challenge in enhancing their customer experience (CX) to stand out in a competitive market. By partnering with Quiq, they implemented a custom AI Agent to transform customer interactions across multiple platforms and create more seamless journeys. This strategic move resulted in a 33% reduction in support-related escalations to human agents.

On the other end of the spectrum, the implementation of Proactive AI and a Product Recommendation engine led to the largest sales day in the company’s history through increased chat sales, showcasing the power of AI in improving efficiency and driving revenue.

Let’s dive into the furniture retailer’s challenges, how Quiq solved them using next-generation AI, the results, and what’s next for this household name in furniture and home goods.

The challenges: CX friction and missed sales opportunities

A leading name in the furniture and home goods industry, this company has long been known for its commitment to quality and affordability. Operating in a sector often the first to signal economic shifts, the company recognized the need to differentiate itself through exceptional customer experience.

Before adopting Quiq’s solution, the company struggled with several CX challenges that impeded their ability to capitalize on customer interactions. To start, their original chatbot used basic natural language understanding (NLU), and failed to deliver seamless and satisfactory customer journeys.

Customers experienced friction, leading to escalations, redundant conversations. The team clearly needed a robust system that could streamline operations, reduce costs, and enhance customer engagement.

So, the furniture retailer sought a solution that could not only address these inefficiencies, but also support their sales organization by effectively capturing and routing leads.

The solution: Quiq’s next-gen AI

With a focus on enhancing every touch point of the customer journey, the furniture company’s CX team embarked on a mission to elevate their service offerings, making CX a primary differentiator. Their pursuit led them to Quiq, a trusted technology partner poised to bring their vision to life through advanced AI and automation capabilities.

Quiq partnered with the team to develop a custom AI Agent, leveraging the natural language capabilities of Large Language Models (LLMs) to help classify sales vs. support inquiries and route them accordingly. This innovative solution enables the company to offer a more sophisticated and engaging customer experience.

The AI Agent was designed to retrieve accurate information from various systems—including the company’s CRM, product catalog, and FAQ knowledge base—ensuring customers received timely, relevant, and accurate responses.

By integrating this AI Agent into webchat, SMS, and Apple Messages for Business, the company successfully created a seamless, consistent, and faster service experience.

The AI Agent also facilitated proactive customer engagement by using a new Product Recommendation engine. This feature not only guided customers through their purchase journey, but also contributed to a significant shift in sales performance.

The results are nothing short of incredible

The implementation of the custom AI Agent by Quiq has already delivered remarkable results. One of the most significant achievements was a 33% reduction in escalations to human agents. This reduction translated to substantial operational cost savings and allowed human agents to focus on complex or high-value interactions, enhancing overall service quality.

Moreover, the introduction of Proactive AI and the Product Recommendation engine led to unprecedented sales success. The furniture retailer experienced its largest sales day for Chat Sales in the company’s history, with an impressive 10% of total daily sales attributed to this channel for the first time.

This outcome underscored the potential of AI-powered solutions in driving business growth, optimizing efficiency, and elevating customer satisfaction.

Results recap:

  • 33% reduction in escalations to human agents.
  • 10% of total daily sales attributed to chat (largest for the channel in company history).
  • Tighter, smoother CX with Proactive AI and Product Recommendations woven into customer interactions.

What’s next?

The partnership between this furniture brand and Quiq exemplifies the transformative power of AI in redefining customer experience and achieving business success. By addressing challenges with a robust AI Agent, the company not only elevated its CX offerings, but also significantly boosted its sales performance. This case study highlights the critical role of AI in modern business operations and its impact on a company’s competitive edge.

Looking ahead, the company and Quiq are committed to continuing their collaboration to explore further AI enhancements and innovations. The team plans to implement Agent Assist, followed by Voice and Email AI to further bolster seamless customer experiences across channels. This ongoing partnership promises to keep the furniture retailer at the forefront of CX excellence and business growth.