What are AI Agents? A Guide to Types and Capabilities

Every week, someone asks me: "Guilherme, is AI going to make your low-code skills obsolete?" It's a valid question, especially when you see headlines about AI writing code. For a moment, even I wondered if all those hours spent mastering n8n, Make, and building complex automations were suddenly on borrowed time. But then I started integrating. Not just using AI, but embedding it into my low-code workflows. What I found wasn't replacement, but a superpower. Instead of spending days configuring intricate logic, I'm now using AI agents to draft initial setups, analyze data patterns, or even generate responses for chatbots built with Typebot and ManyChat. It's like having an incredibly smart co-pilot. My clients aren't asking for less automation; they're asking for smarter automation. They want CRMs that predict customer needs, sales assistants that understand nuances, and processes that adapt on the fly. This isn't about AI taking over; it's about AI making my low-code/no-code solutions – whether on n8n or Make – infinitely more powerful and intelligent. The truth is, AI amplifies what we do. It handles the repetitive, analytical heavy lifting, freeing me to focus on the strategic business outcomes. It makes building intelligent assistants, optimizing processes, and creating custom solutions faster and more effective than ever. So, no, AI isn't replacing low-code. It's making it essential for building the next generation of intelligent systems. What's your take? Are you seeing AI as a threat or an ally in your work?

Your LinkedIn feed is full of AI 'playbooks' promising instant productivity gains. Yet 86% of manufacturers never fully implement their AI projects. The problem isn't the technology. It's the approach. The Lean Learning Collective believes most AI deployments fail because they prioritize flashy features over fundamentals: ❌ Complex 'black box' solutions nobody understands ❌ No consideration for permissions or data governance ❌ Zero plan for ongoing maintenance and updates ❌ Built by techies, ignored by actual users The companies that succeed with AI do the opposite. They build 'boring' workflows that just work. ✅ Start with one high-impact problem (like predictive maintenance on a single machine) ✅ Involve shop floor workers in the design process ✅ Document everything like your job depends on it ✅ Use simple, modular components that can evolve One UK auto parts manufacturer took this approach with visual QA. Instead of a complex custom system, they used off-the-shelf tech + cloud AI. Result: Defect rates dropped from 3% to 0.5%. £250k annual savings. The best AI isn't the smartest AI. It's the AI your team can actually use, maintain, and improve. What's one manual process in your operation that could benefit from 'boring' automation? Your next AI win doesn’t need to be flashy, it needs to work. 
Discover how to build AI workflows your team will actually use.
🔗 Explore The Lean Learning Collective Link in the Comments👇

Ask any team that’s built AI systems about their original timeline and budget estimates, then ask about actual timeline and budget. The gap is rarely less than 50%. Often it’s 200–300%. This isn’t because teams are bad at estimation... it’s because AI projects have fundamentally different dynamics than traditional software dev. AI projects involve inherent uncertainty that traditional software doesn’t face. But that doesn’t mean estimation is useless. It means estimation should account for AI-specific dynamics: - Data work dominates timeline - Integration is harder than model building - Edge cases and performance requirements emerge late - Team capacity is lower than theoretical maximum - Complexity accumulates during development If you build these factors into your estimates, you’ll set realistic expectations that enable AI success rather than optimistic projections that guarantee disappointment. Try my free AI estimation tool here: https://lnkd.in/guPH6-rZ

→ 𝐓𝐡𝐞 𝐇𝐢𝐝𝐝𝐞𝐧 𝐏𝐚𝐭𝐡 𝐭𝐨 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐘𝐨𝐮𝐫 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭: 𝐍𝐨 𝐂𝐨𝐝𝐞 𝐍𝐞𝐞𝐝𝐞𝐝 What if I told you that building a powerful AI agent doesn’t require a single line of code? Sounds like a mystery, doesn’t it? But behind every smart AI, there’s a roadmap - a clear, step-by-step journey that anyone can follow. 𝐋𝐞𝐭’𝐬 𝐝𝐢𝐯𝐞 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐧𝐨-𝐜𝐨𝐝𝐞 𝐫𝐨𝐚𝐝𝐦𝐚𝐩 𝐭𝐨 𝐜𝐫𝐞𝐚𝐭𝐞 𝐲𝐨𝐮𝐫 𝐨𝐰𝐧 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭: • Foundation & Prep Start by defining clear goals. What problem will your AI solve? Understand your use case thoroughly before moving forward. • Platform Selection Choose a no-code AI platform that fits your needs - look for ease of use, integration options, and scalability. • AI Model & Data Select the right AI model for your task. Gather and prepare quality data, as your model’s brain depends on the information you feed it. • Agent Design Lay out the user experience and interaction flow. Design how your AI agent communicates and responds. • Configuration Set up parameters and connect your data sources - no code, just clicks and configurations. • Testing & Iteration Test rigorously. Identify flaws and improve continuously. Your AI becomes smarter every iteration. • Deploy & Monitor Launch your agent in the real world. Monitor performance and optimize over time. • Monitor & Optimize Keep an eye on behavior and user feedback. Refine to maintain effectiveness and relevance. Unlocking AI is not a secret anymore. It’s a roadmap that’s accessible to all, regardless of coding skills. 👉 𝐃𝐌 me for 𝐜𝐚𝐫𝐞𝐞𝐫 𝐠𝐮𝐢𝐝𝐚𝐧𝐜𝐞/ 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐀𝐈 𝐬𝐞𝐭 𝐮𝐩 👉 𝐉𝐨𝐢𝐧 the community to stay updated on new 𝐆𝐞𝐧𝐀𝐈-𝐀𝐠𝐞𝐧𝐭𝐢𝐜𝐀𝐈 advancements. Link in comments section Follow Ujjyaini Mitra for more insights on Enterprise Gen AI

Why Most AI Automation Projects Fail in the First 90 DaysEveryone's rushing to automate with AI, but 73% of implementations fail because they skip this crucial foundation step. Last month, a SaaS founder reached out after their "AI transformation" became a $50K learning experience. They'd built chatbots, automated emails, and connected 12 different tools. The result? More chaos than before. Here's what they missed (and what most companies get wrong): 🔍 They automated broken processes instead of fixing them first. You can't put AI lipstick on a operational pig. Before any automation, we map current workflows, identify bottlenecks, and streamline the human process. Think of it like this: AI amplifies what you already do. If your process is messy, AI will make it messier—just faster. The companies that succeed with AI automation start with process clarity, not technology complexity. What's one process in your business that feels chaotic but you're tempted to "just automate"?

Most CEOs can’t tell the difference between automation and AI agents. Here’s the truth: Agentic AI is the next step, but few get it right. Most companies are still stuck on old automation. And, it’s not their fault These terms often get thrown around like they’re interchangeable. But they’re not. Here’s the breakdown: → Automation: - "If this happens, then do that." - No thinking, just following rules. - Think: A robot on an assembly line. → AI Agents: - TRUST decision-making. - Intelligent decisions based on context. - Think: A personal assistant who knows your preferences. Here's the best part: The most effective workflows today aren’t pure automation or AI agents. They’re AGENTIC WORKFLOWS. What does that mean? Agentic workflows combine the best of both worlds: → AI agents make informed decisions when needed (hello intelligence!) → Automations handle predictable, repetitive tasks (goodbye human error!) But why is this so important? Businesses that adapt to agentic workflows will outpace those clinging to traditional methods! It’s all about understanding what your business needs and learning how to communicate those needs to AI systems. You can't fall behind! Hit me with a DM or comment "Agents" and apply for the chance to be picked for a free AI audit 👇

Your LinkedIn feed is full of AI 'playbooks' promising instant productivity gains. Yet 86% of manufacturers never fully implement their AI projects. The problem isn't the technology. It's the approach. The Lean Learning Collective  believes most AI deployments fail because they prioritize flashy features over fundamentals: ❌ Complex 'black box' solutions nobody understands ❌ No consideration for permissions or data governance ❌ Zero plan for ongoing maintenance and updates ❌ Built by techies, ignored by actual users The companies that succeed with AI do the opposite. They build 'boring' workflows that just work. ✅ Start with one high-impact problem (like predictive maintenance on a single machine) ✅ Involve shop floor workers in the design process ✅ Document everything like your job depends on it ✅ Use simple, modular components that can evolve One UK auto parts manufacturer took this approach with visual QA. Instead of a complex custom system, they used off-the-shelf tech + cloud AI. Result: Defect rates dropped from 3% to 0.5%. £250k annual savings. The best AI isn't the smartest AI. It's the AI your team can actually use, maintain, and improve. What's one manual process in your operation that could benefit from 'boring' automation? Your next AI win doesn’t need to be flashy, it needs to work. 
Discover how to build AI workflows your team will actually use. 
🔗 Explore The Lean Learning Collective Link in the comments👇

The real challenge executives face with AI has little to do with models or data. It’s rooted in mindset. They try to manage AI like traditional software when the ground rules have shifted. ↳ Software 1.0 was predictable: Explicit code, thorough testing, slower delivery, and high stability. ↳ Software 2.0 introduced learning: Models trained on data, new failure modes like drift and bias. ↳ Software 3.0 changes the process again: You describe outcomes in natural language, development speeds up, while maintenance and governance get harder. As software becomes easier to build, it becomes harder to sustain. Every gain in speed brings new layers of operational complexity, monitoring, and cost. Teams that succeed treat governance, testing, and maintenance as first-class work from day one. They set expectations, define ownership, and build review loops around AI behavior, not just code reviews. AI will transform software, but applying a 1.0 mindset in a 3.0 world leads to systems that ship fast and break faster. Are you updating your software playbook for the AI era?

𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐯𝐬 𝐀𝐈 𝐓𝐨𝐨𝐥𝐬: 𝐀 𝐐𝐮𝐢𝐜𝐤 𝐂𝐨𝐦𝐩𝐚𝐫𝐢𝐬𝐨𝐧 ➤ 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬 & 𝐅𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲 -AI Agents handle complex, multi-step tasks autonomously. -They adapt in real-time to changes in context and goals. -Best suited for workflow automation and decision-making. -AI Tools are built for simple, single-purpose functions. -Operate within fixed parameters and require direct input. ➤ 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 & 𝐃𝐞𝐬𝐢𝐠𝐧 AI Agents integrate APIs, tools, and memory modules dynamically. -Maintain context, reason through decisions, and learn from outcomes. -AI Tools are stateless, operate in isolation, and follow predefined logic. -No learning or adaptation unless explicitly reprogrammed. ➤ 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐲 & 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 -Agents chain tasks, pursue objectives, and operate independently. -Require minimal human involvement after initial setup. -Tools rely heavily on user input and execute only what's instructed. -No autonomous task chaining or independent reasoning. ➤ 𝐔𝐬𝐞𝐫 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 & 𝐂𝐨𝐧𝐭𝐫𝐨𝐥 -Agents are backend-driven or managed via high-level dashboards. -Learn from feedback and continuously improve performance. -Tools offer direct interfaces but retain no memory of past actions. -Users control every step and must configure tasks repeatedly. 𝐒𝐮𝐦𝐦𝐚𝐫𝐲: Use AI Agents for adaptive, autonomous systems that run intelligently with minimal oversight. Use AI Tools for manual, focused tasks where precision and control are prioritized. credit:- Habib Shaikh follow:- Tech In Nutshell

Ever wondered how AI agents actually learn and self-optimize behind the scenes? Unlike traditional automation, Agentic AI doesn’t just follow static rules. It learns from every interaction, outcome, and error. Each cycle makes it smarter. Here’s how it works: 1. Observation: The agent monitors patterns in data, workflows, and user inputs. 2. Evaluation: It compares results against goals, identifying what worked and what failed. 3. Adaptation: Using reinforcement learning and feedback loops, it adjusts its strategies for better accuracy. 4. Optimization: Over time, it builds an internal knowledge base to make faster, more precise decisions without human intervention. This self-learning loop is what makes Agentic AI adaptive. It evolves as your business data evolves. No more rigid automations that break when conditions change. Here is how to practise this in your system/workflows: 1. Train your AI agents with quality data. 2. Set clear goals for every task. 3. Let the system iterate and learn from real outcomes. At CamRojud, we design and integrate Agentic AI systems that manage complex digital workflows from SEO optimization to campaign management while continuously improving performance on their own. Your team focuses on creativity and strategy. The AI agents handle execution, monitoring, and optimization. How would an adaptive AI agent transform your daily workflow? Share your thoughts or experiences in the comment. Let’s talk about building AI that learns, thinks, and delivers smarter results. Read more https://lnkd.in/d3u3TCFP