How to Differentiate AI From Traditional Bots

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 

I came across a new framework that brings clarity to the messy world of AI agents with a 6-level autonomy hierarchy. While most definitions of AI agents are binary (it either is or isn't), a new framework from Vellum introduces a spectrum of agency that makes far more sense for the current AI landscape. The six levels of agentic behavior provide a clear path from basic to advanced: 𝐋𝐞𝐯𝐞𝐥 0 - 𝐑𝐮𝐥𝐞-𝐁𝐚𝐬𝐞𝐝 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰 (𝐅𝐨𝐥𝐥𝐨𝐰𝐞𝐫) No intelligence—just if-this-then-that logic with no decision-making or adaptation. Examples include Zapier workflows, pipeline schedulers, and scripted bots—useful but rigid systems that break when conditions change. 𝐋𝐞𝐯𝐞𝐥 1 - 𝐁𝐚𝐬𝐢𝐜 𝐑𝐞𝐬𝐩𝐨𝐧𝐝𝐞𝐫 (𝐄𝐱𝐞𝐜𝐮𝐭𝐨𝐫) Shows minimal autonomy—processing inputs, retrieving data, and generating responses based on patterns. The key limitation: no control loop, memory, or iterative reasoning. It's purely reactive, like basic implementations of ChatGPT or Claude. 𝐋𝐞𝐯𝐞𝐥 2 - 𝐔𝐬𝐞 𝐨𝐟 𝐓𝐨𝐨𝐥𝐬 (𝐀𝐜𝐭𝐨𝐫) Not just responding but executing—capable of deciding to call external tools, fetch data, and incorporate results. This is where most current AI applications live, including ChatGPT with plugins or Claude with Function Calling. Still fundamentally reactive without self-correction. 𝐋𝐞𝐯𝐞𝐥 3 - 𝐎𝐛𝐬𝐞𝐫𝐯𝐞, 𝐏𝐥𝐚𝐧, 𝐀𝐜𝐭 (𝐎𝐩𝐞𝐫𝐚𝐭𝐨𝐫) Managing execution by mapping steps, evaluating outputs, and adjusting before moving forward. These systems detect state changes, plan multi-step workflows, and run internal evaluations. Examples like AutoGPT or LangChain agents attempt this, though they still shut down after task completion. 𝐋𝐞𝐯𝐞𝐥 4 - 𝐅𝐮𝐥𝐥𝐲 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 (𝐄𝐱𝐩𝐥𝐨𝐫𝐞𝐫) Behaving like stateful systems that maintain state, trigger actions autonomously, and refine execution in real-time. These agents "watch" multiple streams and execute without constant human intervention. Cognition Labs' Devin and Anthropic's Claude Code aspire to this level, but we're still in the early days, with reliable persistence being the key challenge. 𝐋𝐞𝐯𝐞𝐥 5 - 𝐅𝐮𝐥𝐥𝐲 𝐂𝐫𝐞𝐚𝐭𝐢𝐯𝐞 (𝐈𝐧𝐯𝐞𝐧𝐭𝐨𝐫) Creating its own logic, building tools on the fly, and dynamically composing functions to solve novel problems. We're nowhere near this yet—even the most powerful models (o1, o3, Deepseek R1) still overfit and follow hardcoded heuristics rather than demonstrating true creativity. The framework shows where we are now: production-grade solutions up to Level 2, with most innovation happening at Levels 2-3. This taxonomy helps builders understand what kind of agent they're creating and what capabilities correspond to each level. Full report https://lnkd.in/gZrGb4h7

Workflows vs. AI Agents. The REAL difference and why the Distinction Matters: Automation is evolving, but not all automation is created equal. The divide between workflows and AI agents highlights a crucial shift in how we think about problem-solving. Take the workflow in the image as an example: It processes user-fed inputs in a structured, predefined way. Look at it as a content generator: the user inputs the data, and the workflow executes based on static logic. It's efficient, but limited to the instructions it's been given. Now consider the orchestrator AI Agent model: This is where AI agents excel. Unlike workflows, orchestrators dynamically initiate tasks the moment new data arrives. They break down complex problems, assign tasks to specialized agents, and synthesize results, all without requiring a user to trigger the process manually. For example: Workflow Use Case: Aggregating user-submitted data to create a consistent output, such as generating content from a structured feed. Orchestrator Use Case: Analyzing incoming data streams autonomously, identifying necessary actions, and delegating those actions to task-specific agents. Here’s the real difference: Workflows are manual and static, while AI agents are autonomous and adaptive. Workflows depend on the user to dictate the process, but AI agents take the lead, interpreting and acting on data without predefined paths. This distinction is more relevant than ever. This topic has sparked increasing discussion in AI and automation circles. Some lean on workflows for their simplicity, while others see AI agents as the future of intelligent, adaptive systems. It’s clear this debate is shaping the future of how businesses automate complex processes. What’s your take? For those using AI agents or workflows in production, where are you seeing the best results? I’d love to hear your perspective.

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.

Lately, the term AI Agent has been popping up everywhere—but what actually makes an AI agent different from a regular chatbot or model? I came across this helpful guide that breaks it down beautifully. Here’s a simple summary in plain language: Core Principles Behind AI Agents: - Autonomy: They can act without constant human instructions. - Planning: They break big goals into small steps. - Reflection: They learn from past actions to improve. - Statefulness: They remember past conversations or tasks. - Prompting: They react to input or questions to decide what to do next. Key Capabilities That Make Agents Smart: - Task Decomposition: Breaking complex tasks into manageable pieces. - Memory Retrieval: Pulling information from memory to stay relevant. - Tool Use: Calling APIs, web browsers, or databases to get things done. - Observability: Tracking decisions and actions for transparency. Memory Types in Agents: - Short-Term Memory: Keeps track of recent conversations. - Long-Term Memory: Stores knowledge across different sessions. - Semantic Memory: Holds facts and meanings. - Procedural Memory: Remembers how to perform tasks. - Episodic Memory: Remembers past experiences or events. Different Agent Roles: - Researcher: Finds information from the web or data sources. - Planner: Breaks tasks into steps. - Executor/Coder: Performs the steps, like coding or summarizing text. Design Approaches: - Tool-Centric Agents: Rely heavily on external tools. - Model-Centric Agents: Depend more on language understanding and internal reasoning. - Many modern systems combine both for balance. How Agents Learn: They improve through feedback loops and self-reflection, making them smarter over time without constant human correction. The Agent Loop (ReAct Framework): Perceive → Plan → Act → Learn – a continuous cycle that makes agents adaptive and autonomous. There’s also a growing ecosystem of frameworks like LangChain, AutoGen, CrewAI, and others helping developers build smarter agents faster. AI agents are more than chatbots—they’re evolving toward systems that can think, plan, and act, much like human collaborators. Which of these agent concepts are you exploring in your work?

Ever wondered about the differences between traditional automation, AI automation, and AI agents? It’s a question I get asked a lot, so I put together this infographic, for all of you! 1️⃣ Traditional Automation ↳ Primarily rule-based: think straightforward RPA (Robotic Process Automation), basic factory robots, or simple scripted IT tasks. ↳ Great for repetitive processes with predictable, static conditions. ↳ Still struggles with unpredictable changes, requiring frequent reprogramming by humans. ↳ Tools: UiPath, Blue Prism, Automation Anywhere — these remain the dominant RPA solutions, but they’re increasingly integrating AI for tasks like document understanding. 2️⃣ AI Automation ↳ How It Works: Machine Learning and other AI approaches to learn from data and adapt with minimal human intervention. ↳ Adapts to changing inputs—like email spam filters that get better over time or AI chatbots that refine responses. ↳ Examples: Fraud detection systems, recommendation engines, advanced chatbots. ↳ Tools/ Frameworks: → Gumloop: A rising platform that lets teams prototype, test, and deploy AI models with minimal coding → Zapier: For connecting AI-driven workflows to thousands of apps 3️⃣ AI Agents ↳ How they differ: These go beyond pattern recognition to reason, plan, and act autonomously. ↳ They actively make contextual decisions in real time, learning from ongoing interactions. ↳ Examples: Self-driving cars orchestrating traffic decisions, personal AI research assistants scouring data for insights, or “smart” systems that can optimize supply chains on the fly. ↳ Tools/ Frameworks: → CrewAI: Focuses on real-time collaboration and multi-agent systems with a Pythonic design → LangChain: A framework that enables developers to build applications powered by large language models, suitable for creating custom AI agents. → AutoGen: An open-source Python-based framework by Microsoft, designed for developers to create advanced AI agents with minimal coding → RASA: Open-source framework for building intelligent chatbots and voice assistants with advanced NLU → LangGraph: LangChain-created tool for building and managing complex generative AI agent workflows using graph-based architectures. → OpenAI Swarm: Experimental framework for lightweight, customizable multi-agent systems focusing on flexible task delegation and coordination. 𝌭 Foundational LLMs/SLMs : → Open-source models from Mistral AI models, Microsoft Phi, Google Gemma models, DeepSeek AI models, Perplexity R1-1776, Meta llama models, Alibaba Group Qwen models → Closed-source models from OpenAI, Anthropic, Perplexity, Google 🚀 Top-Inference providers- Fireworks AI, Groq, Cerebras Systems I’d love to hear your experiences: Have you implemented AI agents recently? Any favorite frameworks or tools you think are game-changers? Share below 👇 -------- Share this post with your network ♻️ Follow me (Aishwarya Srinivasan) for more AI insights, news, and educational content!

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

Two key concepts are often used interchangeably: 𝗔𝗜 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 and 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀. But they’re not the same! Understanding their differences can help you leverage them effectively.  🔹 𝗔𝗜 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻:  Think of this as traditional 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 powered by AI. It follows pre-defined rules, executes repetitive tasks, and streamlines processes—but lacks adaptability.  Example: AI-powered chatbots that answer FAQs, automated document processing, or AI-enhanced RPA in back-office workflows.  🔹 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀:  AI Agents are 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻, 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 capable of making decisions, learning from interactions, and adapting dynamically to new situations. They don’t just execute tasks—they 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝘇𝗲, 𝗹𝗲𝗮𝗿𝗻, 𝗮𝗻𝗱 𝗲𝘃𝗼𝗹𝘃𝗲.  ✅ Example: Autonomous customer support agents, AI-driven software engineers coding solutions, or 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗻𝗴 𝗘𝗧𝗟 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝗶𝗻 𝗔𝗜-𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝗱𝗮𝘁𝗮 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀.  𝗞𝗲𝘆 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀:   - AI Automation → 𝗙𝗼𝗹𝗹𝗼𝘄𝘀 𝗿𝘂𝗹𝗲𝘀, 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗮𝗯𝗹𝗲, 𝗿𝗲𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲   - AI Agents → 𝗚𝗼𝗮𝗹-𝗼𝗿𝗶𝗲𝗻𝘁𝗲𝗱, 𝗮𝗱𝗮𝗽𝘁𝗶𝘃𝗲, 𝗱𝘆𝗻𝗮𝗺𝗶𝗰  AI Agents are shaping the future of intelligent systems, bridging the gap between automation and 𝘁𝗿𝘂𝗲 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝘆.  What are your thoughts? This amazing Gif is created by Manthan Patel

How is an AI agent different from a chatbot? I explored this question at TechCrunch Disrupt yesterday alongside Marc Boroditsky, during our session on standing out in enterprise sales. Essentially, while both systems can leverage natural language technologies to provide human-like responses, chatbots only follow pre-defined rules to complete prescribed tasks. Behind the scenes, these systems are scripted and limited by the bounds of exactly what they were designed to do. On the other hand, AI agents are reactive, adapting to novel situations and making context-aware decisions by consulting external stakeholders or other agents, incorporating vanilla tools, and drawing on past learnings. These systems can be grounded in unique datasets and then learn and grow from direct user interaction. In today's AI Atlas, I dive into an innovative open-source framework promising to expand the development of practical AI agents. Known as Husky, the system was recently developed by researchers at the University of Washington, Meta, and the Allen Institute. Husky even matches the performance of state-of-the-art models such as ChatGPT in certain use cases, despite being only a fraction of the size, making it an exciting foundation for an agentic future.

Not Everything with AI is an #AIAgent —Let’s Get It Right. AI is everywhere, but true AI agents are still rare. 🚀 Most AI-powered tools are just automation—not autonomous agents. Here’s the difference: ❌ A #chatbot answering queries? Not an AI agent. ❌ A recommendation system on #Netflix? Not an AI agent. ❌ An AI-powered automation tool? Still not an AI agent. What #Defines a True AI Agent? ✅ It understands its environment. ✅ It reasons and plans its actions. ✅ It executes autonomously without human intervention. ✅ It learns and adapts from past experiences. The reality? 🔹 90% of so-called "AI agents" are just rule-based automation or basic AI models. 🔹 True AI agents require strategic reasoning, complex decision-making, and self-correction. 🔹 They must handle uncertainty and operate toward goal-oriented outcomes. AI is evolving fast. But precision matters. Let’s call things what they really are. Most tools today are AI-powered solutions—not true AI agents. 💡 The future will bring more autonomy, but we’re not fully there yet. Are you seeing real AI agents in action, or just advanced automation? Let’s discuss. 👇 Follow the link to read full article : https://lnkd.in/gXkjvKwn #AI #ArtificialIntelligence #Automation #AIagents #Technology