Conversational AI for Enhanced User Engagement

Human conversation is interactive. As others speak you are thinking about what they are saying and identifying the best thread to continue the dialogue. Current LLMs wait for their interlocutor. Getting AI to think during interaction instead of only when prompted can generate more intuitive and engaging Humans + AI interaction and collaboration. Here are some of the key ideas in the paper "Interacting with Thoughtful AI" from a team at UCLA, including some interesting prototypes. 🧠 AI that continuously thinks enhances interaction. Unlike traditional AI, which waits for user input before responding, Thoughtful AI autonomously generates, refines, and shares its thought process during interactions. This enables real-time cognitive alignment, making AI feel more proactive and collaborative rather than just reactive. 🔄 Moving from turn-based to full-duplex AI. Traditional AI follows a rigid turn-taking model: users ask a question, AI responds, then it idles. Thoughtful AI introduces a full-duplex process where AI continuously thinks alongside the user, anticipating needs and evolving its responses dynamically. This shift allows AI to be more adaptive and context-aware. 🚀 AI can initiate actions, not just react. Instead of waiting for prompts, Thoughtful AI has an intrinsic drive to take initiative. It can anticipate user needs, generate ideas independently, and contribute proactively—similar to a human brainstorming partner. This makes AI more useful in tasks requiring ongoing creativity and planning. 🎨 A shared cognitive space between AI and users. Rather than isolated question-answer cycles, Thoughtful AI fosters a collaborative environment where AI and users iteratively build on each other’s ideas. This can manifest as interactive thought previews, real-time updates, or AI-generated annotations in digital workspaces. 💬 Example: Conversational AI with "inner thoughts." A prototype called Inner Thoughts lets AI internally generate and evaluate potential contributions before speaking. Instead of blindly responding, it decides when to engage based on conversational relevance, making AI interactions feel more natural and meaningful. 📝 Example: Interactive AI-generated thoughts. Another project, Interactive Thoughts, allows users to see and refine AI’s reasoning in real-time before a final response is given. This approach reduces miscommunication, enhances trust, and makes AI outputs more useful by aligning them with user intent earlier in the process. 🔮 A shift in human-AI collaboration. If AI continuously thinks and shares thoughts, it may reshape how humans approach problem-solving, creativity, and decision-making. Thoughtful AI could become a cognitive partner, rather than just an information provider, changing the way people work and interact with machines. More from the edge of Humans + AI collaboration and potential coming.

I recently spent 3 weeks trying to build a voice AI assistant for a client project. The result? A robotic experience with 2-3 second delays that made users want to hang up immediately. Then I discovered Agora's Conversational AI Engine, and everything changed. Here's what blew my mind: → 650ms Response Time: That's faster than most humans respond in conversation. No more awkward pauses that kill user engagement. → Real Interruption Handling: Users can actually interrupt the AI mid-sentence—just like talking to a real person. Revolutionary for natural conversation flow. → Complete Control: Bring your own LLM (OpenAI, Claude, Gemini, custom), your own TTS (Microsoft, ElevenLabs), your own everything. Zero vendor lock-in. → Built for Scale: Running on Agora's SD-RTN that handles 6+ billion voice minutes monthly. From prototype to production without breaking a sweat. The game-changer? Three lines of code. That's literally all it takes to add voice AI to your app. Built on the open-source TEN framework, they've abstracted away months of development complexity. Real-world impact I'm seeing: • Healthcare AI companions providing 24/7 emotional support • Retail assistants that actually understand complex product questions   • Gaming NPCs with dynamic personalities that remember your history • Enterprise tools that scale without losing the human touch If you're building anything that needs voice interaction, skip the months of R&D headaches. Your users will thank you for conversations that feel genuinely human. Your DevOps team will thank you for infrastructure that just works. Ready to experience the difference? → https://lnkd.in/dinYCzYA #VoiceAI #ConversationalAI #DeveloperTools #RealTimeAI #Agora #AIEngineering #TechInnovation

I've tested over 20 AI agent frameworks in the past 2 years. Building with them, breaking them, trying to make them work in real scenarios. Here's the brutal truth: 99% of them fail when real customers show up. Most are impressive in demos but struggle with actual conversations. Then I came across Parlant in the conversational AI space. And it's genuinely different. Here's what caught my attention: 1. The Engineering behind it: 40,000 lines of optimized code backed by 30,000 lines of tests. That tells you how much real-world complexity they've actually solved. 2. It works out of the box: You get a managed conversational agent in about 3 minutes that handles conversations better than most frameworks I've tried. 3. Conversation Modeling Approach: Instead of rigid flowcharts or unreliable system prompts, they use something called "Conversation Modeling." Here's how it actually works: 1. Contextual Guidelines: ↳ Every behavior is defined as a specific guideline. ↳ Condition: "Customer wants to return an item" ↳ Action: "Get order number and item name, then help them return it" 2. Controlled Tool Usage: ↳ Tools are tied to specific guidelines ↳ No random LLM decisions about when to call APIs ↳ Your tools only run when the guideline conditions are met. 3. Utterances Feature: ↳ Checks for pre-approved response templates first ↳ Uses those templates when available ↳ Automatically fills in dynamic data (like flight info or account numbers) ↳ Only falls back to generation when no template exists What I Really Like: It scales with your needs. You can add more behavioral nuance as you grow without breaking existing functionality. What's even better? It works with ALL major LLM providers - OpenAI, Gemini, Llama 3, Anthropic, and more. For anyone building conversational AI, especially in regulated industries, this approach makes sense. Your agents can now be both conversational AND compliant. AI Agent that actually does what you tell it to do. If you’re serious about building customer support agents and tired of flaky behavior, try Parlant.

“Let me explain the issue again…I was saying…” Does this sound familiar? We’ve all been there: stuck on the phone or chat, explaining the same problem to a new support agent for the third, fourth, or fifth time, feeling unheard. But customer service isn’t just about solving problems. It’s about making people feel heard. Yet, far too often, support interactions feel robotic, cold, and disconnected. You’re bounced between departments. Asked to repeat yourself again and again. Given a ticket number instead of a real solution. And the worst part? No one seems to remember your last conversation. This isn’t just inefficient; it’s deeply frustrating and exhausting, and it shows a lack of empathy. Customer service must go beyond transactions. It should tap into attentive empathy, truly listening to customers, acknowledging their frustrations and cognitive empathy, and offering relevant solutions based on past interactions and emotional context. So how do we do that at scale? OpenAI’s latest update is a step in that direction. ChatGPT can now remember past conversations across sessions. This simple upgrade unlocks a smarter, more empathetic future for customer service. Imagine this: • Your support agent already knows what you’ve been through • They pick up right where you left off • They tailor responses to your preferences and pain points This is what modern, emotionally intelligent service should feel like. And the data speaks volumes: 🔹 76% of customers say repeating themselves is their #1 frustration 🔹 81% prefer brands that personalize the experience With AI memory in play, customer service teams can now: • Offer personalized support journeys • Reduce friction in every interaction • Proactively engage based on past pain points • Build long-term trust through seamless continuity For businesses, this means: • Smarter, AI-powered systems that improve with every touchpoint • Consistent journeys that feel human even when powered by machines • Stronger retention through empathy-led engagement If you’re a forward-thinking company, here’s what to do: • Invest in AI tools with conversational memory • Redesign support flows to feel continuous, not fragmented • Train agents to collaborate with AI as empathy amplifiers • Prioritize data transparency and privacy to build lasting trust Because when customers feel understood, they don’t just stay, they advocate. #AI #ChatGPT #customerexperience #CX #KSA #SaudiArabia

Conversational AI is transforming customer support, but making it reliable and scalable is a complex challenge. In a recent tech blog, Airbnb’s engineering team shares how they upgraded their Automation Platform to enhance the effectiveness of virtual agents while ensuring easier maintenance. The new Automation Platform V2 leverages the power of large language models (LLMs). However, recognizing the unpredictability of LLM outputs, the team designed the platform to harness LLMs in a more controlled manner. They focused on three key areas to achieve this: LLM workflows, context management, and guardrails. The first area, LLM workflows, ensures that AI-powered agents follow structured reasoning processes. Airbnb incorporates Chain of Thought, an AI agent framework that enables LLMs to reason through problems step by step. By embedding this structured approach into workflows, the system determines which tools to use and in what order, allowing the LLM to function as a reasoning engine within a managed execution environment. The second area, context management, ensures that the LLM has access to all relevant information needed to make informed decisions. To generate accurate and helpful responses, the system supplies the LLM with critical contextual details—such as past interactions, the customer’s inquiry intent, current trip information, and more. Finally, the guardrails framework acts as a safeguard, monitoring LLM interactions to ensure responses are helpful, relevant, and ethical. This framework is designed to prevent hallucinations, mitigate security risks like jailbreaks, and maintain response quality—ultimately improving trust and reliability in AI-driven support. By rethinking how automation is built and managed, Airbnb has created a more scalable and predictable Conversational AI system. Their approach highlights an important takeaway for companies integrating AI into customer support: AI performs best in a hybrid model—where structured frameworks guide and complement its capabilities. #MachineLearning #DataScience #LLM #Chatbots #AI #Automation #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gFjXBrPe

 Is This the Future of Human-AI Interaction? Sesame's "Voice Presence" is Astonishing. Have you ever truly felt like you were having a conversation with an AI? Sesame, founded by Oculus co-founder Brendan Iribe, is pushing the boundaries of AI voice technology with its Conversational Speech Model (CSM). The results are striking. As The Verge's Sean Hollister noted, it's "the first voice assistant I've ever wanted to talk to more than once." Why? Because Sesame focuses on "voice presence," creating spoken interactions that feel genuinely real and understood. What's the potential impact for businesses? Enhanced Customer Service: Imagine AI assistants that can handle complex inquiries with empathy and natural conversation flow. Improved Accessibility: More natural voice interfaces can make technology accessible to more users. Revolutionized Content Creation: Voice models like Maya and Miles could open up new audio and video content possibilities. Training and Education: Interactive AI tutors could provide personalized and engaging learning experiences. The most impressive part? In blind listening tests, humans often couldn't distinguish Sesame's AI from real human recordings. #AI #ArtificialIntelligence #VoiceTechnology #Innovation #FutureofWork #CustomerExperience #MachineLearning #SesameAI

Can a virtual assistant truly transform how we interact with technology? Join me as I welcome Alan Bekker, PhD, CEO of eSelf.AI, a visionary in the AI space. From his early days in machine learning to founding Voka, a pioneering voice bot company later acquired by Snapchat, Alan’s journey is packed with insights. In this episode, we explore how #AI is reshaping customer interactions through multilingual, human-like virtual assistants. Alan shares practical strategies for integrating AI into businesses, overcoming user adoption challenges, and optimizing for fast ROI. 🔹 The rise of AI-powered voice & visual agents 🔹 How AI integrates seamlessly with existing business systems 🔹 Multilingual capabilities driving better customer interactions 🔹 Visual engagement as a game-changer for communication 🔹 Common pitfalls when implementing AI solutions 🔹 Future trends in AI adoption We dive into eSelf.AI’s ground breaking face-to-face visual conversational AI engine, which is revolutionizing industries by providing real-time, multilingual AI agents. Alan also introduces eService's ESOF, a cutting-edge product redefining customer engagement by merging AI with business systems. Imagine custom AI agents offering 24/7 multilingual support, seamlessly integrated with CRMs for real-time insights. Beyond technology, we also explore the ethical challenges of human-like AI interactions and why visual engagement is key to creating natural, intuitive experiences. From streamlining transactions to crafting next-level customer journeys, eSelf.AI’s innovations are setting new standards in AI-driven service. Don’t miss this conversation on the future of AI-powered customer interactions! #AI #CustomerExperience #ConversationalAI #VirtualAssistants #BusinessInnovation

Is #VoiceAI the #NextBigThing for businesses since the #internet boom of the 90s? 🤖 I genuinely believe so. We're on the cusp of a massive change in how we interact with technology. Remember when every business suddenly needed a website? I see the same thing happening with voice AI by 2026. We're moving beyond simple voice commands. We're building #AIAgents that can truly understand conversational context, speak like a human, and, most importantly, act. Think about an Voice enabled #ConversationalAI that can: 💎 Search the web for you 💎 Update your customer database in real-time 💎 Send follow-up emails 💎 Book appointments and manage calendars This isn't science fiction; it's what's possible right now. I recently built a #demo for a client to showcase exactly this. I created an outbound calling system that contacts their customers and partners, asks relevant business questions, and instantly updates their database with the new information. No more manual data entry, no more outdated records. Check out the video to see it in action! 👇 This is just one example. The potential use cases are huge: 📞 Automated Surveys: Gather customer feedback on products and services effortlessly. 🤝 Sales & Outreach: Pitch new solutions to potential customers. 🛎️ Virtual Office Assistant: Handle incoming calls, answer customer questions, and book appointments 24/7. The goal is to create a better overall #CustomerExperience and free up valuable time for your team, resulting in #OperationalEfficiency and #Productivity jump. 👨💻 Curious about the #tech behind it? 👨💻 In simple terms, the system uses a backend to initiate a call through a #WebRTC platform (I used LiveKit, but others work too). This platform then connects to our backend "worker node" that asks the #Livekit to dial the number via Twilio #SIP to reach the person on the regular #PSTN phone network. Once the call is answered, the system cleverly adds the Voice AI agent into the conversation. From there, the person and the #AI can talk naturally. When the call ends, the system cleans up everything automatically. The best part? It's built to scale. By containerizing the worker nodes (think of them as little self-contained packages of code using #Docker) and managing them with something like #Kubernetes, the system can handle thousands of simultaneous calls without breaking a sweat. If things get busy, it just spins up new workers to handle the load. It's a seamless and powerful #architecture. I'm incredibly passionate about AI in general and its potential to revolutionize how businesses operate. If you found this #demo #insightful, I'd appreciate it if you'd like, comment, or share it with your network! What are your thoughts on this? Do you see Voice AI becoming a standard tool for businesses? Let's discuss in the comments! ⬇️ #VoiceAI #ML #MachineLearning #ArtificialIntelligence #FutureOfBusiness #Innovation #Automation #OutboundCalling #AISDR #LLM #CustomerService #AISales #AgenticAI

How Close Are We to Truly Natural AI Voices? ◉ I recently tested Sesame’s Conversational Speech Model (CSM), an experimental AI designed to create more natural, context-aware voice interactions. ↳ Their approach leverages multimodal learning with transformers to enhance expression and comprehension. Some aspects felt remarkably human. Others still need work. ↳ Test it for free here: https://lnkd.in/eerFM9xG ↳ it reminded me Moshi AI by Kyutai few months ago ◉ CSM’s Core Focus Areas: ↳ Conversational dynamics: Managing natural timing, pauses, and emphasis. ↳ Emotional intelligence: Understanding and responding to emotional cues. ↳ Contextual awareness: Adjusting tone and style based on the situation. ↳ Consistent personality: Maintaining a coherent presence. ◉ Key Takeaways: ✅ Smooth conversational flow with low latency and clear end-of-thought transitions. ❌ Sometimes too chatty, reducing efficiency in real-world tasks. ❌ Struggles with precise details like names, emails & phone numbers. ❌ English-only – Limited in accents and cultural context. ❓ Unclear usage cost ◉ Wait but... what’s the Real Challenge in Voice AI? It’s not just about sounding realistic—it’s about delivering real value to callers and reducing drop rates within the first few seconds. ↳No call duration = No opportunity to deliver value. ↳No context/adaptation = AI feels artificial, leading to dropped calls. ◉ Take a look at this chart: ↳ Without context, users often can't tell the difference between AI and human speech (50/50 split). ↳ But once context is introduced—like the specific details and history of a conversation—the AI becomes noticeably less convincing. ◉ So, beyond voice quality, several other factors impact engagement: 1. Smart call routing – Ensuring the right AI or agent answers. 2. Personalization – "Hello Mr. X, glad you called back." 3. Listen - like humans never enough ;-) 4. Problem resolution – Offering real solutions, not just responses. 🔁 These four steps must be repeated until the caller is satisfied. ◉ Unlike AI demos, real-world business cases aren’t about just having a conversation—they’re about driving meaningful outcomes. ◉ Where does CSM stand in the broader Voice AI landscape VS Speech-to-text x LLM x Text-to-speech ? ↳ For function calling, multilingual support, and reliable automation → [stt x llm x tts] ↳ For superior voice naturalness and human-like flow → CSM CSM has the potential to redefine AI-driven conversations, but right now, STT x LLM x TTS remains the go-to architecture for real-world business use cases. What’s the missing piece in your Voice AI stack? Let’s break it down. #VoiceAI #AITechnology #ConversationalAI --- 👋 Hi, I’m Maxime Grandjean, CEO of CallShift.ai ↳ We handle 1000+ calls/day for our clients, solving real-world Voice AI challenges. ↳ I post about the Voice AI shift happening right now. ↳ Follow me for practical insights & strategies.

Are you still relying solely on human interaction for lead conversion? Let's explore the transformative power of an AI SMS bot in lead engagement. AI SMS bots are not just automated responders; they are intelligent conversationalists. They understand and respond to leads in real-time, making interactions efficient and personalized. Imagine a tool that works 24/7, never misses a message, and tirelessly nurtures leads. The beauty of AI SMS bots lies in their ability to adapt conversations based on responses. They're programmed to recognize cues and steer the conversation towards your objective. This approach ensures that every lead receives prompt and relevant engagement. The result? Higher conversion rates without overwhelming your sales team. Check out the attached video and try to spot the difference. You'll see how seamlessly the AI bot picks up on what the person is saying. Notice its skill in gently guiding the conversation towards the desired goal. This isn't just about replacing human effort; it's about enhancing it. AI SMS bots are a must-have in your digital arsenal for effective and efficient lead conversion. What are your thoughts on integrating AI technology in lead management?