The rise of AI agents and agentic AI

Transforming digital experiences and business strategies

The automation has come a long way since the early days of simple, rule-based chatbots like ELIZA, developed at MIT in the 1960s. Today, AI-powered conversational interfaces and intelligent automation systems are redefining how businesses interact with customers, optimise workflows, and enhance user experiences. As AI technology evolves, companies are increasingly integrating AI agents and agentic AI to drive smarter automation and unlock new opportunities for efficiency and innovation.

Real-world use cases of AI agents and IVR systems

The rapid evolution of AI presents an unprecedented opportunity for businesses to redefine digital experiences, optimise operations, and create value-driven customer interactions. Most applications of AI we've seen lately can be grouped into a few main use cases:

• IT system management: Identifying and mitigating potential issues to prevent system downtime.
• Supply chain optimisation: Dynamic adjustments in logistics and inventory based on real-time market conditions.
• Autonomous customer support: Engaging users in human-like conversations to resolve queries efficiently.
• AI-enhanced customer interactions: Improving voice interactions, reducing call center workloads, and delivering personalised service

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The role of IVR systems in automated customer interactions

IVR (Interactive Voice Response) systems have been a staple in customer service for decades, allowing businesses to automate telephone-based interactions. These systems have evolved significantly, integrating AI to enhance customer experiences and streamline operations. AI-driven IVR systems use natural language processing (NLP) and speech recognition technologies to enable more dynamic and human-like conversations, reducing the need for live agents.

Key applications of IVR systems:

• Automated call routing: Directs callers to the appropriate department or representative based on voice inputs.
• Self-service transactions: Allows customers to check account balances, update information, or make payments without human assistance. 
• AI-powered customer support: Enhances traditional IVR with AI-driven responses, reducing wait times and improving resolution efficiency.

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Frameworks powering AI agents

To manage the complexities of AI implementation, businesses rely on robust frameworks that facilitate seamless integration with existing data ecosystems.

• LlamaIndex: Enables AI agents to access, process, and retrieve data efficiently from business databases.
• LangChain: Supports multi-agent workflows and external API integrations for extended automation capabilities.
• Haystack: Specialises in conversational search and information retrieval, ideal for handling vast datasets and knowledge management.
• Google Cloud contact center AI: Uses AI to enhance IVR capabilities, providing intelligent call routing, speech-to-text conversion, and real-time customer sentiment analysis.
• Five9 intelligent virtual agent: An advanced AI-powered IVR solution that leverages machine learning to personalise customer interactions and automate call handling.

When it comes to language models, OpenAI’s GPT-4 leads in conversational AI, but competitors like Google’s PaLM, Meta’s LLaMA, and Anthropic’s Claude are gaining traction for their contextual understanding, advanced reasoning, and ethical AI design.

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The rise of agentic AI

Agentic AI represents the next stage of AI evolution—moving beyond task automation to autonomous decision-making and problem-solving. These intelligent systems can proactively analyse data, adapt to dynamic environments, and execute complex workflows with minimal human oversight.

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The future of AI agents and agentic AI in UX design

As AI continues to evolve, the line between human-driven and machine-driven tasks is becoming increasingly blurred. This shift is paving the way for new ways of working, unlocking opportunities for emerging areas and features where UX designers can play a crucial role. To create seamless, engaging, and responsible digital experiences, businesses must strike the right balance between automation and human expertise while carefully considering ethical implications.

Success stories

Leading companies are increasingly leveraging AI-powered agents to streamline customer service, enhance user engagement, and provide seamless support.

A few standout examples of AI agents:

• KLM Royal Dutch Airlines: AI chatbot BlueBot (BB) assists with flight bookings, queries, and travel updates via the website, app, and social media.
• ING: AI assistant Inge helps this large Dutch bank's customers check balances, make transfers, and get quick banking support through the website and app.
• Zalando: This fashion e-commerce giant uses AI chatbots for product recommendations, order tracking, and returns, ensuring seamless customer service.

Other user experience enhancing applications of AI agents:

• Cognigy: Automates up to 70% of customer interactions through conversational AI, reducing response times and operational costs.
• ServiceNow: Uses AI to streamline IT, HR, and enterprise workflows, ensuring predictive issue resolution and seamless automation.
• Intercom: Delivers personalised customer interactions by leveraging AI to analyse user data and offer tailored responses.
• Zendesk: Enhances support workflows with AI-driven chat, ticket routing, and knowledge management tools.
• HubSpot: Integrates AI to automate marketing, sales, and customer service operations, optimising lead generation and outreach.
• Amazon: Deploys AI for e-commerce recommendations, contact center automation (Amazon Connect), and intelligent voice interfaces (Amazon Lex).
• IBM Watson: Offers AI-powered insights and natural language processing for industries like healthcare and finance.

Challenges and ethical considerations

Despite their potential to save costs, scale quickly and enhance personalisation, AI agents come with challenges that businesses must address to ensure responsible deployment. As regulations evolve, businesses must align with frameworks like the UK government’s Generative AI Guidelines to ensure AI’s lawful, ethical, and transparent use.

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Potential pitfalls

Realising AI's full potential requires strategic implementation, ethical foresight, and a commitment to human-centered design. The Rathenau Institute warns that generative AI poses risks such as:

• Discrimination: Biased training material can lead to biased and potentially discriminatory responses.
• Threats to intellectual property: AI can generate content that may infringe copyrights, blur ownership rights, and create legal uncertainties due to outdated IP laws.‍
• Lack of human empathy: AI, while intelligent, lacks the emotional nuance of human interactions.‍
• Risk of errors, misinformation, unintended consequences: Misinterpretations and AI hallucinations can lead to incorrect responses and customer dissatisfaction.‍
• Data privacy and security: Ethical concerns surrounding AI’s access to sensitive user data require careful oversight.‍
• Integration complexity: Implementing AI into legacy systems can be costly and resource-intensive.

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Lessons from AI failures

Recent high-profile AI mishaps highlight the importance of ongoing refinement and oversight.

• New York City’s legal chatbot misinformed users about employment laws, exposing risks in regulatory AI applications.
• General Motors’ AI chatbot was manipulated into offering luxury cars at unsustainable discounts.
• DPD’s customer service bot went rogue, responding inappropriately to users and damaging brand reputation.

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Our recommendations for applying AI in UX

The rapid evolution of AI presents an unprecedented opportunity for businesses to redefine digital experiences, optimise operations, and create value-driven customer interactions. However, realising its full potential requires strategic implementation, ethical foresight, and a commitment to human-centered design. Our recommendations:

• Emphasise UX-centric AI integration: AI should enhance—not fully replace—human interactions, ensuring a frictionless user experience.
• Invest in AI transparency and explainability: Users should understand how AI-driven decisions impact them.
• Adopt Agile AI strategies: Businesses must continuously test, refine, and improve AI agent deployments.
• Monitor ethical and regulatory compliance: AI implementations should align with emerging industry regulations and best practices.

As an agency, we help businesses navigate the AI revolution—ensuring technology works in harmony with user needs, business goals, and ethical AI practices. The future of AI is bright, but only if it is shaped with care, transparency, and accountability.