Charafeddine Mouzouni’s Post

The next wave of AI will not be generic. It will be vertical. Here is the problem with most AI tools today: they are built for everyone, which means they do not really work for anyone. They are incredible tools, but I do not believe they create any sort of competitive advantage. They are great for demos, not so great for workflows. Vertical AI is different. It is tuned for one environment at a time. It understands how your operations actually run, the systems you use, the language your teams rely on, and the constraints you face. It does not just generate text. It executes your playbook. Look at logistics, where bottlenecks come from delays and manual entry. AI voice connected to your systems can remove that friction. Or field operations, where hands are busy but information still needs to move. Voice becomes the bridge, capturing intent and triggering action without breaking flow. These are real challenges businesses face every day, and they are being solved not by generic tools, but by voice-driven vertical AI. And here is the important part: building a vertical AI is not as difficult as it sounds. If you are specific about your prompt, specific about the context for the AI, and specific about your delivery, you are already most of the way there. Delivery could be voice, text, mobile, desktop, all delivered within a custom application. This is where I think, for lack of a better word, the idea of vibe coding / building comes in. It is about capturing intent, flow, and decisions, and turning them into lightweight, specialized applications that verticalize your knowledge and requirements. The advantage is not in the model. Everyone can rent GPT or Claude. The advantage is in how you translate your workflows into agentic applications that work exactly the way you need them to. The companies that move now will win. Start with two or three workflows that are repetitive, manual, and critical. Build around those. Prove value quickly. Then expand. This is how voice, vertical AI, and vibe coding / building will reshape how businesses actually work.

AI projects fail when the outputs don’t deliver real value. That’s why successful AI teams obsess over quality. And when it comes to AI outputs, the key to knowing whether you’re delivering quality is evaluations. Evals are like software QA but for AI systems. They're a structured process to check outputs for - accuracy - consistency - ethical alignment - business value - any other checks that matter to your business At 360Learning, we’re rolling out an AI-driven GTM intelligence system, so I built a simple Retool app to formalize our evals. It shows the input and AI output, then asks users five quick questions about accuracy, relevance, and insight. The result? We not only validate how well the system works, we also align users on what “good” looks like and build excitement in the process. Note, this is a big topic and there are many sophisticated methods for AI evaluations (including automated evals, LLM-as-judge, etc.) But don't let that stop you from getting started. We started simple. You don’t even need a custom app to do this. A Google Doc or spreadsheet is enough. The important part is: don’t skip evaluations. They may be the difference between a flashy demo that dies in production and an AI system that actually transforms how people work.

In the AI race to automate everything, most teams end up with a shiny product that can’t scale. Or survive. Here’s the real showstopper: When things break, the development team has no idea where to start fixing. AI products are non-deterministic by nature. Bugs don’t show up in neat lines of code. I saw a great mental model from Aishwarya Naresh Reganti and Kiriti Badam in Lenny Rachitsky’s Newsletter. It nails a tension I run into all the time in AI product strategy: the push for more AI “agency” (letting the AI act on its own) versus the need for human “control” (making sure we keep it safe and useful). Most teams want to speed through the journey. But moving too fast skips all the learning and trust-building that makes a product stick. Here’s their cheat sheet for scaling AI agency, based on a simple customer support example: 𝗩1: 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗵𝗶𝗴𝗵 𝗰𝗼𝗻𝘁𝗿𝗼𝗹, 𝗹𝗼𝘄 𝗮𝗴𝗲𝗻𝗰𝘆. AI just routes support tickets to humans. Nothing fancy, no risk. You learn the basics in a safe way, build data, and set expectations. 𝗩2: 𝗠𝗲𝗱𝗶𝘂𝗺 𝗰𝗼𝗻𝘁𝗿𝗼𝗹, 𝗺𝗲𝗱𝗶𝘂𝗺 𝗮𝗴𝗲𝗻𝗰𝘆. AI drafts replies, but a human always reviews and sends. You learn where it messes up and what real value it adds. The product gets better, step by step. 𝗩3: 𝗟𝗼𝘄 𝗰𝗼𝗻𝘁𝗿𝗼𝗹, 𝗵𝗶𝗴𝗵 𝗮𝗴𝗲𝗻𝗰𝘆. AI can now auto-resolve common tickets. Only now does it earn real autonomy. The focus is trust: where does it still break, and when do you pull in a human? What I love about this model: it’s not just technical. It’s about building trust, getting real user data, and creating something people want to pay for, not just something that looks good in a demo. You de-risk each step, validate trust, and set up the next phase with proof. The full article is a must-read for any leader in this space.

“What I love about this model: it’s not just technical. It’s about building trust, getting real user data, and creating something people want to pay for, not just something that looks good in a demo. You de-risk each step, validate trust, and set up the next phase with proof”

You want to solve real problems with AI? You need COMPLETE and CONSISTANT results... This is the only way: You will need create an automation flow where you have full control. Here is a simple use case that did not worked: - I wanted a table with each car model from a list, with name, price and boot size in L - the list was not even that long (below 100) result of a car rental aggregator - no chatbot or generic agent was able to provide the complete table - they were always missing between 25% and 75% of the car models So generic agentic scraping completely failed. And probably will still fail for years to come. What works like a charm is a specific scraper tool for THIS site in an custom automation for THIS use case. Can be no-code flow in n8n or (vibe)Code ... whatever works for the complexity of the task There are 2 reasons "generic" chatbots will be always unreliable: 1. they try to cover every use case, hence they cannot go deep on any of them 2. If you have large data sets, they will never go through all your data - because that would cost too much for them (openAI, Anthropic, Google) (and large data is actually smaller than you think) How to solve it: You must control the flow's: 1. complexity: specialized prompts, specialized tools for that PARTICULAR data set and use case 2. volume: cycle over small chunks of data with the precision of the tools above. (you decide when to stop... you pay for the tokens) So you can process all the data uninterrupted. If anyone finds a way to make this use case work with a generic chatbot or generic agent please let me know Antony Martini Alexandru Dan Magdalena O.

Most AI founders waste $10K+ on tools that look good on demos but break in production. Here are 7 boring tools that serious teams actually rely on: 1. LangGraph Agent workflows without spaghetti. Teams use it to define multi-step reasoning in a way that's explainable and repeatable. -- 2. Langfuse Observability for prompts. Tracks accuracy, latency, and cost - the three metrics that decide if your product feels polished or broken. -- 3. Helicone LLM usage dashboard. Helps finance teams understand where the money is going before the cloud bill becomes unmanageable. -- 4. LiteLLM One API that speaks to every model. Gives teams the freedom to swap between GPT, Claude, Llama, or Gemini without rewriting code. -- 5. Unstructured .io The unsung hero. Parses PDFs, Word docs, and emails into clean text chunks so RAG pipelines don't choke. -- 6. HoneyHive Feedback layer. Lets you capture real user ratings on AI output, then loop it back into training and evaluation. -- 7. Claude 3.5 Still the go-to when context length and careful reasoning matter - especially for policy, contracts, or multi-doc QA. -- None of these trend on Product Hunt. But they're the ones real operators quietly lean on to ship stable AI systems. 🚀 P.S. Want more battle-tested tool recommendations like this? We share production-ready stacks and what actually works in our free AI Product Accelerator community. ↪️ Link in the comments to join. 100% free.

🚀 *Agentic AI* is exploding, but which framework should you bet on? If you’ve tried building AI agents, you know the options are multiplying: LangGraph, LangChain, Autogen, CrewAI, Make.com, n8n… but they’re not interchangeable. Here’s how to make sense of the chaos: 🦜🔄 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 Think enterprise-grade orchestration. Graph-based, stateful, long-running workflows with loops, branching, and persistent memory. Perfect when your agent system needs durability + complexity. 🦜🔗 *LangChain* The OG. Great for chaining prompts, tools, and RAG. If you just need a chatbot, simple agent, or MVP, start here. 🟥🟦🟩🟨 *AutogenAI (Microsoft)* Built for multi-agent collaboration. If you want agents to negotiate, coordinate, and tackle big tasks together, this is your go-to. 🤖 *CrewAI* Lightweight and flexible. Assemble “crews” of role-specific agents quickly, while keeping granular control. Fast deployments, minimal dependencies. 🥢 *Make* Visual, no-code automation for business users. Connect AI to CRMs, reports, SaaS tools—without writing a single line of code. 🟣🔄🟤 *n8n* Open-source, node-based automation. Great for RAG-powered workflows and deep data integrations with a visual touch. 💡 *Bottom line:* ▪️Want enterprise complexity? → LangGraph ▪️Need fast AI app prototyping? → LangChain ▪️Building collaboratively? → Autogen or CrewAI ▪️Prefer drag-and-drop ? → Make.com or n8n The right choice depends on your workflow complexity, control needs, and dev resources. Agentic AI is not one-size-fits-all!

🚀 *Agentic AI* is exploding, but which framework should you bet on? If you’ve tried building AI agents, you know the options are multiplying: LangGraph, LangChain, Autogen, CrewAI, Make.com, n8n… but they’re not interchangeable. Here’s how to make sense of the chaos: 🦜🔄 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 Think enterprise-grade orchestration. Graph-based, stateful, long-running workflows with loops, branching, and persistent memory. Perfect when your agent system needs durability + complexity. 🦜🔗 *LangChain* The OG. Great for chaining prompts, tools, and RAG. If you just need a chatbot, simple agent, or MVP, start here. 🟥🟦🟩🟨 *AutogenAI (Microsoft)* Built for multi-agent collaboration. If you want agents to negotiate, coordinate, and tackle big tasks together, this is your go-to. 🤖 *CrewAI* Lightweight and flexible. Assemble “crews” of role-specific agents quickly, while keeping granular control. Fast deployments, minimal dependencies. 🥢 *Make* Visual, no-code automation for business users. Connect AI to CRMs, reports, SaaS tools—without writing a single line of code. 🟣🔄🟤 *n8n* Open-source, node-based automation. Great for RAG-powered workflows and deep data integrations with a visual touch. 💡 *Bottom line:* ▪️Want enterprise complexity? → LangGraph ▪️Need fast AI app prototyping? → LangChain ▪️Building collaboratively? → Autogen or CrewAI ▪️Prefer drag-and-drop ? → Make.com or n8n The right choice depends on your workflow complexity, control needs, and dev resources. Agentic AI is not one-size-fits-all!

🚀 *Agentic AI* is exploding, but which framework should you bet on? If you’ve tried building AI agents, you know the options are multiplying: LangGraph, LangChain, Autogen, CrewAI, Make.com, n8n… but they’re not interchangeable. Here’s how to make sense of the chaos: 🦜🔄 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 Think enterprise-grade orchestration. Graph-based, stateful, long-running workflows with loops, branching, and persistent memory. Perfect when your agent system needs durability + complexity. 🦜🔗 *LangChain* The OG. Great for chaining prompts, tools, and RAG. If you just need a chatbot, simple agent, or MVP, start here. 🟥🟦🟩🟨 *AutogenAI (Microsoft)* Built for multi-agent collaboration. If you want agents to negotiate, coordinate, and tackle big tasks together, this is your go-to. 🤖 *CrewAI* Lightweight and flexible. Assemble “crews” of role-specific agents quickly, while keeping granular control. Fast deployments, minimal dependencies. 🥢 *Make* Visual, no-code automation for business users. Connect AI to CRMs, reports, SaaS tools—without writing a single line of code. 🟣🔄🟤 *n8n* Open-source, node-based automation. Great for RAG-powered workflows and deep data integrations with a visual touch. 💡 *Bottom line:* ▪️Want enterprise complexity? → LangGraph ▪️Need fast AI app prototyping? → LangChain ▪️Building collaboratively? → Autogen or CrewAI ▪️Prefer drag-and-drop ? → Make. com or n8n The right choice depends on your workflow complexity, control needs, and dev resources. Agentic AI is not one-size-fits-all!

2025 isn’t just the year of AI Agents. We’ve entered the decade of AI Agents. ⬇️ LLMs were the preview. Agents are where AI finally gains hands and feet. 👉 It doesn’t just answer, it executes. 👉 It doesn’t just chat, it delivers. That’s where true disruption begins. Of course, it can feel overwhelming. New repos, new frameworks, new launches — every single day. Nobody can keep pace with it all. But here’s the key: you don’t need to learn everything at once. What you need is structure. That’s why Aishwarya Srinivasan’s roadmap is so valuable. Here’s a breakdown of the 10 Levels of AI Agents: ⸻ 1️⃣ Foundations of GenAI 📚 Transformers, attention, tokenization, pre-training vs. fine-tuning (OpenAI, DeepMind). 2️⃣ Prompting & Reasoning 📚 Chain-of-thought, few-shot learning, safety techniques (Anthropic, Cohere). 3️⃣ Retrieval-Augmented Generation (RAG) 📚 Embeddings, chunking, vector databases like Pinecone, Weaviate, FAISS (used by Shopify, Notion). 4️⃣ Tools & Orchestration 📚 Frameworks such as LangChain, LangGraph, CrewAI; function calling and workflow design (IBM, Microsoft). 5️⃣ Agent Construction 📚 From simple task agents to autonomous loops (AutoGPT, ReAct; applied by Adept, Replit). 6️⃣ Memory Integration 📚 Buffer, episodic, entity, and event-driven memory (LangGraph, MemGPT, ElevenLabs for voice). 7️⃣ Multi-agent Systems 📚 Research assistants, dev teams, financial bots working in coordination (Hugging Face, BloombergGPT). 8️⃣ Feedback & Improvement 📚 RLHF, RLAIF, reward modeling (OpenAI, Anthropic, Google DeepMind). 9️⃣ Safety & Protocols 📚 MCP, guardrails, traceability, policy enforcement (IBM, Salesforce, OpenAI). 🔟 Production Deployment 📚 Frameworks like FastAPI/Gradio, infrastructure optimization (NVIDIA GPUs/CPUs), monitoring (LangSmith, Weights & Biases, Databricks). ⸻ The age of AI Agents is here. The only real question: are you building with structure, or getting lost in the noise?