Conversational AI Systems

Conversational AI platforms provide today's benchmark for self-service and AI-driven customer engagement. The core capabilities of these platforms span 4 areas: • Integrations with back-end systems, communication channels, and knowledge sources. • AI technologies for speech and natural language processing, understanding, and generation (NLP/NLU/NLG). • No-code conversation design environment. • Toolsets for defining, testing, and refining intents and entities. In just 18 months, GenAI has reshaped the conversational AI market. Platforms have undergone two rounds of evolution—sometimes requiring a complete rebuild of functions—and must keep pace with relentless innovation. A new generation of platforms is emerging, driven by key trends and evolving needs: 1) Proprietary NLP/U is no longer the differentiator—platforms must orchestrate best-of-breed AI models and enable the combination of multiple specialized models. 2) GenAI simplifies intent management, but a new toolset is needed to customize and optimize models beyond basic prompting and RAG. 3) Voice AI requires best-in-class speech-to-text, text-to-speech, and speech-to-speech to meet performance and experience demands. 4) Platforms need to support both transactional and informational interactions. 5) Deterministic workflows will dominate CX and sales in the short term, but autonomous agents will redefine application development. 6) Integration capabilities will evolve into orchestrated, agent-driven ecosystems with robust governance. 7) Platforms must manage context over longer conversations. 8) Orchestration must extend beyond interactions and AI to enable sophisticated AI-human collaboration. 9) Platforms need to enable faster iterations and continuous expansion of use cases The tension between disaggregating functions for independent evolution and assembling an expanding set of technologies makes it difficult to predict what platforms will look like in a few years. Not all providers will successfully transition—some, burdened by technical debt, will be forced to pivot toward specialized solutions. When evaluating platforms, the key is to define the flexibility you truly need and make tradeoffs accordingly. A purpose-built solution may be a better fit than a broad platform, allowing you to leverage the vendor’s deep domain expertise. But that doesn’t eliminate the need for rigorous validation of their technology stack and architecture. Given that 'platform' is a catch-all term in vendor messaging, it’s essential to cut through the noise and classify offerings accurately. As conversational AI evolves toward the orchestration of conversations, technologies, and human-AI collaboration, use these trends as strategic lenses to guide your decisions. Above all, prioritize openness to navigate this evolving landscape. I trimmed the article to fit this post; the full version is linked in the first comment. #conversationalai #ai #cx #salestech

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.

I learned a lot about AI assistants recently: they are the easiest thing to demo but the hardest thing to actually build. I had Vanessa Lee from Shopify on the show a couple of weeks ago, and this insight has stuck with me ever since. It perfectly captures why so many companies are struggling with AI product development right now. You can create an impressive AI demo in a few hours using vibe coding. Show it to executives, and they'll immediately see the potential. Then the real work begins. Unlike traditional features with predictable user flows, conversational AI has to handle infinite variations of what users might ask or do. Vanessa explained that conversational AI is inherently unpredictable "because users can ask anything." That's exactly why Shopify invested heavily in LLM-powered evaluation systems and committed to ruthless iteration. They understood the complexity gap from day one. Most companies get trapped in what I call the "demo trap." They promise AI features based on early prototypes without understanding the full development complexity ahead. The result? Blown timelines, frustrated teams, and products that never live up to the initial demo. Don't let the ease of creating AI demos fool you into underestimating the product development challenge. What's your experience been with AI product development? Have you fallen into the demo trap?

I just got quoted in Forbes ☺️ ... in a piece that explores the rise of conversational video AI - a market projected to reach $82.46B by 2034 - where agents can now see your face, hear your tone, and respond in real time. But instead of obsessing over avatars & realism, we focused on the part of ML that’s far messier and far more important... Epistemic Humility! Right now, most of the industry is racing to make AI _look_ more human: • Better facial expressions • Smoother voice inflections • Real-time visual & emotional cues ... but imo true intelligence isn't just about bluffing coherence. It’s about: → Explicitly tracking uncertainty → Admitting “I don’t know” when data is missing → Reasoning through ambiguity instead of faking confidence → Moving from surface mimicry to joint attention, shared context, intent, and understanding As I shared with Forbes, the next leap isn’t hyper-realism. It’s AI that can maintain joint attention, reason under uncertainty, and participate in conversation like a human - not just perform like one! Key takeaways for anyone building or deploying AI systems: • Overconfident models are riskier than uncertain ones • Human-like isn’t a UX problem - it’s a modeling & evaluation problem • Reliability matters more than realism in real-world systems • The ability to say I DON'T KNOW is a feature, not a failure The future of conversational AI isn’t about perfect mimicry. It’s about integrity. Systems that are honest about their limits & designed to handle uncertainty responsibly. Honored to be featured alongside brilliant researchers & leaders in this space. Thank you, Forbes, for featuring my voice ✨ #ConversationalAI #AgenticAI #TechTrends #Forbes

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

How can businesses get the most from conversational and agentic AI? Both are reshaping how organizations work and serve customers, but they deliver impact in different ways. The opportunity for leaders is knowing where each shines and how to combine them for maximum ROI.  🔹 Conversational AI thrives in the moment. It understands and responds naturally during interactions to answer questions, guide customers to the right resources, and gather details in real time. 🔹 Agentic AI takes it further. Built with skills like memory, reasoning, and autonomous action, it can recognize signals, predict needs, and trigger workflows without manual input. Picture a support call: conversational AI greets a customer, identifies the issue, and provides initial guidance. Agentic AI detects urgency in their tone, escalates the case, and updates records across systems instantly. When organizations pair the responsiveness of conversational AI with the autonomy of agentic AI, they create interactions that are more personalized, efficient, and impactful. At RingCentral, we’re building on two decades of voice expertise to make this pairing even more powerful with solutions like our AI Receptionist and RingSense, so every conversation can become an engine for long-term growth.

Last week, a colleague was struggling to add voice features to his fintech app. After weeks of wrestling with ASR→LLM→TTS orchestration, dealing with latency issues, and trying to make different APIs play nice together, he was ready to give up. That got me thinking - why is adding voice AI still so complicated in 2025? Why are developers spending months on infrastructure instead of building features that matter? And that's when I discovered Agora's Conversational AI Engine - and honestly, it's a game-changer. Here's what makes it different: 🚀 𝗦𝗲𝘁𝘂𝗽 𝗶𝗻 𝗠𝗶𝗻𝘂𝘁𝗲𝘀, 𝗡𝗼𝘁 𝗠𝗼𝗻𝘁𝗵𝘀 - Three lines of code to add voice AI to your app - Built on open-source TEN framework - No complex orchestration headaches ⚡ 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 - ~650ms total latency (that's lightning fast!) - Runs on Agora's SD-RTN (80 billion minutes of voice and video streams monthly) - Auto-scales from 1 to 1M users without breaking 🔧 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗙𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆 - Use any LLM: OpenAI, Claude, Gemini, or your custom model - Any TTS: Microsoft, ElevenLabs, or roll your own - Zero vendor lock-in - maintain full control 🎧 𝗔𝘂𝗱𝗶𝗼 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 - Neural noise suppression that actually works - Users can interrupt the AI mid-sentence (just like real conversations!) - Echo cancellation for crystal-clear audio What really impressed me? It handles real-world conditions. Coffee shop noise, poor connectivity, multiple speakers - the Conversational AI Engine delivers consistent performance when everything else fails. If you're building anything with voice, skip the months of R&D headaches. Your users will get natural conversations, your team will get infrastructure that just works. And if you found this useful, share it with your developer community so more people can build voice-first experiences without the pain! Explore the Conversational AI Engine → https://lnkd.in/gUvPGNes [Asset: Agora-sizzle-built-for-devs+cta.mp4] #VoiceAI #DeveloperTools #ConversationalAI #APIDevelopment #TechInnovation #Agora #RealTimeAI #BuildingWithAI #DeveloperExperience

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

Interesting paper. Ever wonder why AI assistants sometimes nail simple questions but completely drop the ball on complex requests? New research from Tsinghua University reveals the problem: current dialogue systems are optimized for isolated turns, not the full conversation journey. Here’s what makes this breakthrough different: The Core Problem Traditional reinforcement learning treats every dialogue turn equally. Book a flight? The AI gets the same reward for asking your name as it does for actually completing the booking. The S2RL Solution Step-by-Step Reinforcement Learning introduces intermediate rewards at crucial conversation milestones: - Information gathering phase - Clarification checkpoints - Task completion verification - User satisfaction signals Why This Matters As AI agents move from simple Q&A to complex task execution (scheduling, booking, troubleshooting), understanding the “journey” becomes critical. This research bridges the gap between dialogue management and actual task completion. The future of conversational AI isn’t just about better responses - it’s about better outcomes. What multi-step tasks do you wish AI could handle more smoothly? Read the full paper: https://lnkd.in/d6Agezny #ArtificialIntelligence #MachineLearning #ConversationalAI #ReinforcementLearning #AIResearch #DialogueSystems #NLP