What Distinguishes Agentic AI From Traditional Chatbots

AI Agent vs. Agentic AI: Understanding the Shift from Task Execution to True Autonomy 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 • Designed for single-task execution • Use predefined, static tools • Follow fixed workflows without context awareness • Operate with limited or no memory • Rely on human coordination for retries, planning, or tool selection • Cannot self-reflect or improve their strategy    𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 • Capable of autonomous goal execution • Select tools dynamically based on the task • Break down goals into sub-steps using multi-step reasoning • Retain persistent memory of past actions and user preferences • Collaborate with multiple agents to solve complex tasks • Reflect on outcomes and optimize strategies • Adapt workflows in real time    𝗪𝗵𝗲𝗻 𝗗𝗼 𝗬𝗼𝘂 𝗖𝗼𝗺𝗯𝗶𝗻𝗲 𝗧𝗵𝗲𝗺? The goal is advanced autonomy with modular flexibility. Use AI Agents as modular components within Agentic AI systems Let Agentic AI orchestrate decision-making, planning, and coordination

I've read 100+ pages on AI agents this week. Here's what most people get wrong: People think agents = chatbots. They're not. Agents are AI systems that independently (keyword) execute multi-step workflows with real autonomy. Here's what actually makes an agent: 1. Independent Decision Making - Must control its own workflow execution - Can recognize task completion and correct mistakes  - Knows when to hand control back to humans 2. Real-World Integration - Has access to external tools and systems - Can read data AND take concrete actions - Dynamically selects right tools for each phase 3. Built-in Safety Rails (optional, but recommended)  - Runs concurrent security checks - Filters sensitive data in real-time - Escalates high-risk actions to humans 4. Incremental Complexity - Start with single-agent architecture - Add capabilities through tools, not agents - Only split into multi-agent system when necessary 5. Clear Handoff Protocols - Defined triggers for human intervention - Graceful transitions between agents - Maintains context through transfers Building agents isn't about creating fancy chatbots. It's about automating complex workflows end-to-end with intelligence and adaptability. — Have you seen a “real” AI agent in the wild?  — Enjoyed this? 2 quick things: - Follow me for more AI automation insights - Share this a with teammate 

Traditional GenAI vs Agentic GenAI — A New Era of Intelligence The world of generative AI is evolving rapidly—and we’re now witnessing a major shift from traditional GenAI systems to what’s being called Agentic GenAI. This transformation isn’t just about better outputs—it’s about more intelligent behavior, deeper context, and autonomous action. Here's how they differ: Traditional GenAI Generates direct answers, often in a few steps Relies entirely on user prompts to function Has limited or no memory (stateless) Cannot reason or plan ahead Works solely on pre-trained data—no external tool usage Outputs are highly stochastic and unpredictable Agentic GenAI Handles multi-step problem solving with complex workflows Operates autonomously, triggered by internal/external signals Can reason, decide, and explain its logic Maintains stateful memory across long time periods Uses tools, APIs, databases, and retrieval systems (RAG) Incorporates deterministic chains to ensure accuracy and traceability Why this matters: Agentic GenAI isn’t just about generating content—it’s about creating systems that think, act, and learn over time. This shift will redefine how we build software, manage workflows, and scale businesses. We're moving from AI as a reactive assistant to AI as an active collaborator. Imagine systems that proactively schedule meetings, generate insights, resolve tickets, or run entire workflows—with minimal human intervention. If you're exploring AI in your work—whether for operations, product, customer experience, or automation—understanding this transition is crucial. Are you building with traditional AI, or are you ready for agentic systems? Let’s talk about how this evolution is reshaping industries.

Everyone’s talking about AI agents. But almost no one can explain them simply. Let’s fix that 👇 An AI agent is a program that can: ✅ Sense its environment ✅ Decide what to do ✅ Take action, with or without you Here’s what makes them powerful: 🧠 Memory → Remembers your preferences & stores knowledge ⚙️ Autonomy → Acts independently or with human oversight 👀 Reactivity → Detects what’s happening and responds in real-time 🧰 Tool Use → Makes API calls, browses the internet, writes or runs code 🤝 Collaboration → Works with humans or other agents to get things done Types of AI Agents 🔹 Simple Reflex → Reacts instantly to input (like a sensor) 🔹 Model-Based → Remembers past info to decide 🔹 Goal-Based → Acts toward a specific target 🔹 Utility-Based → Picks the best option 🔹 Learning Agent → Improves over time How AI Agents Work Together 1️⃣ Single-Agent → Like a personal AI assistant 2️⃣ Multi-Agent → Multiple AIs collaborating 3️⃣ Human-Machine → AI supporting human workflows What AI Agents Are Not 🚫 Not just a chatbot 🚫 Not simple automation 🚫 Not rigid workflows They think, adapt, and act, like intelligent teammates. 📌 This visual breaks it down beautifully 👇 Save it. Study it. Share it. 🔁 Repost if this made AI agents finally click ➕ Follow Gabriel Millien for clear, no-jargon breakdowns on AI & the future of work Image credit: ByteByteGo

Day 1: What is Agentic AI? (And How is it Different from Generative AI?) Most people know about Generative AI — tools like ChatGPT, Claude or DALL·E that help create content: words, images, code, presentations. But Agentic AI is something different. Agentic AI doesn’t just create — it acts. It’s designed to: • Understand a goal • Break it down into tasks • Make decisions • Execute actions • Learn and adjust as it works The easiest way to think about it? → Generative AI gives you suggestions. → Agentic AI gets things done. ⸻ Why does this matter for business? Agentic AI shifts AI from being a helper → to being a doer. Example: Let’s say you’re launching a new product. Generative AI: → “Write me a social media post announcing our new product.” Agentic AI: → “Create a social media post, schedule it across all platforms, update the product page on the website, generate internal FAQs for the sales team, draft outreach emails to key clients, monitor engagement, and flag any customer questions that need a human response.” ⸻ → Generative AI gives you content. → Agentic AI runs the playbook. Tomorrow (Day 2), I’ll share examples of where Agentic AI is already showing up in real businesses — with focus on how teams operate.

Is your "AI chatbot" actually driving results, or just shifting gears? Many AI tools today use RAG (Retrieval-Augmented Generation). It's like a manual stick shift – a step up, letting the AI answer questions based on your knowledge base. But you're still doing most of the driving. What if your AI could handle the whole journey? That's the promise of True Agentic AI. It's less stick shift, more self-driving car. Many vendors claim "Agentic AI" by just bolting tools onto RAG. That's not the same. True Agentic AI 🧠 : 👉 Plans multi-step actions. 👉 Acts autonomously, selecting tools. 👉 Checks results & self-corrects. 👉Learns. It moves beyond answering to truly resolving complex customer goals, navigating multi-turn issues autonomously. Agentic AI still uses RAG for knowledge (like GPS uses maps), but adds the crucial layers of reasoning and autonomous action to actually drive. I explored this vital distinction (True Agentic vs. RAG+Tools), the ROI, and guardrails in our latest blog post. This space is evolving fast and the architectures are still evolving. Putting it out here helps us collect important feedback Alhena.ai (formerly Gleen AI) is one of the first companies with a truly Agentic AI system (more details coming soon..). As you explore this space, few questions to ponder over: ➡️ Is your current AI more stick shift or self-driving? ➡️ What defines 'true' Agentic AI for you? ➡️ How crucial is autonomous planning & action? Share your insights below! 👇 (Link to the full blog post in the first comment)

AI Agent vs Agentic AI — What’s the Real Difference? Traditional AI agents were built for static, single-task execution. They follow predefined workflows, rely on hard-coded tools, and require manual coordination. While effective for narrow tasks, they often lack flexibility, memory, and contextual reasoning. Enter Agentic AI — the Next Evolution Agentic AI changes the game. These systems: - Autonomously select tools - Dynamically plan across multiple steps - Collaborate with other agents - Adapt through self-reflection and persistent memory Agentic AI is built for real-world complexity, enabling multi-step reasoning and context-aware decision-making at scale. When Should You Use Both? The real power emerges when you combine traditional AI agents with Agentic AI: ✅ Wrap traditional agents inside Agentic systems for modular, repeatable task handling ✅ Use Agentic AI to orchestrate, reason, and drive adaptive workflows Together, they deliver scalable, intelligent, and reusable systems that balance specialization with autonomy. The future of AI isn’t just automation. It’s orchestration—driven by Agentic Intelligence. #AI #AgenticAI #FutureOfWork #Automation #IntelligentSystems #AIAgents #EnterpriseAI #TechInnovation

The simplest way to understand AI Agents: (for non-technical people) There’s a lot of confusion about what makes an AI system "agentic" vs just an AI tool or workflow. Here's a simple example that breaks it down: 📝 Non-agentic workflow: YOU (a human) create a LinkedIn post using #ChatGPT. You don't like the initial draft, so YOU go back and tweak the prompt multiple times. YOU decide when it's good enough to publish. 🤖 Agentic workflow: You set a goal: "Create a LinkedIn post about AI workflows." The AI AGENT decides it needs quality control, so it independently creates a "Critique Bot.” This “Critique Bot” evaluates the draft, and iterates on the content until it meets quality standards – all without your intervention. This distinction highlights the three critical traits that make something an AI agent: 1️⃣ Reasoning - The agent decides what approach to take 2️⃣ Acting - The agent selects and uses appropriate tools 3️⃣ Iterating - The agent evaluates its own work and improves it In simple terms: An AI tool requires YOU to be the decision-maker. An AI agent becomes the decision-maker itself. #aiagents #googlegemini

𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗗𝗲𝘀𝗶𝗴𝗻: 𝟱 𝗟𝗲𝘃𝗲𝗹𝘀 𝗬𝗼𝘂 𝗡𝗲𝗲𝗱 𝘁𝗼 𝗞𝗻𝗼𝘄 In the rapidly evolving world of AI, understanding the layers of agentic design is crucial. Level 1: Simple Processor – At the foundational stage, the LLM functions purely as a smart text generator. It takes input and returns output without influencing any external processes. Think of a basic chatbot that answers static queries like “What is AI?” Level 2: Router – The LLM steps into a more intelligent role, acting as a decision-maker. It can analyze the input and route the task to the appropriate sub-flow or function, such as directing user complaints to billing or technical support. Level 3: Tool Calling – Here, the agent begins interacting with external tools. It not only interprets the input but also calls APIs or services to fulfill real-world tasks, like booking a cab using a rideshare API. Level 4: Multi-Step Agent – This level involves coordinating a series of actions across different agents or tools. An example is a travel planner AI that can manage flights, hotels, and itinerary creation handling each task through specialized agents in a seamless workflow. Level 5: Fully Autonomous Agent – The pinnacle of agentic design. These agents are capable of generating code, executing it, validating results, and iteratively improving based on feedback. Imagine a self-learning coding assistant that writes, tests, and optimizes a trading bot all on its own. This visual summary not only educates but also serves as a roadmap for developers, researchers, and AI enthusiasts aiming to build smarter, more capable systems. Follow Nikhil Kassetty for more ! AI Frontier Network #AIWorkflow #MultiAgentSystems #AIUseCases #AIEngineering #AgenticAI

AI Agents for Non Technical people.. The other day, I was catching up with a friend who works in a non-technical field. As we were chatting, the topic somehow drifted to AI and this buzzword we keep hearing a lot these days—AI Agents or Agentic AI. He looked at me like I had just started speaking a different language. That’s when I realized how hard it can be to explain these concepts without slipping into jargon.It took me a moment, but I finally broke it down in a way that clicked for him. And that’s when it hit me—why not share this simplified version with my LinkedIn community too? Think of it as AI that’s goal-driven, autonomous, and capable of making decisions—almost like having a smart assistant who gets things done for you. Let’s break it down: 📌 What is Agentic AI? Agentic AI refers to AI systems designed to perform tasks autonomously, often by breaking them into smaller subtasks, reasoning through them, and taking action using tools or APIs. 💡 A Simple Analogy Imagine hiring a personal assistant for a project: You tell them what you need. They figure out the steps. They use their network of contacts and tools to make it happen. They check back with you if needed. Agentic AI does all of this—but digitally. 📌 Key Features of Agentic AI Autonomous Thinking: It doesn't need constant instructions. ↳ Example: Planning a marketing campaign based on your business goals. Tool Utilization: It uses APIs, search engines, or databases to get the job done. ↳ Example: Fetching data, running reports, or scheduling tasks. Memory: It remembers past tasks and uses that knowledge to improve future ones. ↳ Example: Learning your preferences over time to offer better recommendations. Decision-Making: It analyzes data and chooses the best course of action. ↳ Example: Prioritizing tasks based on deadlines and resources. 📌 Where is Agentic AI Used? Customer Support: Automating ticket resolution by finding answers and escalating issues. Project Management: Assigning tasks, tracking progress, and ensuring deadlines are met. Data Analysis: Identifying trends and creating reports for business insights. Personal Assistants: Managing schedules, emails, and reminders. 🔑 Why It Matters Agentic AI isn’t just about automation—it’s about intelligent automation. It enables businesses and individuals to save time, reduce errors, and focus on what matters most. 💬 What would you use Agentic AI for? Share your thoughts below! ♻️ Share 👍 React 💭 Comment to make Agentic AI simple and accessible for everyone. #AgenticAI #aiagents