Generative AI and Agentic AI: From Answering Questions to Taking Action

Source: Dev.to

Pipeline & Prompts | Byte size guides on DevOps, Cloud and AI I used to spend hours before important workshops and client meetings pulling together materials. Downloading presentation decks, saving links, reading through documents, trying to hold enough in my head to answer questions confidently in the room. Then I discovered NotebookLM. NotebookLM is a generative AI tool from Google that lets you upload your own documents, presentations, and links and then have a conversation with all of that material as if it were a knowledgeable colleague who had read everything perfectly. Before a recent conference I uploaded every presentation, every link, every document related to the topics being discussed. Then during the event, when someone asked me a question I was not sure about, I could query my own private knowledge base in seconds and get a precise answer drawn directly from the materials I had loaded. I also started using generative AI to write meeting agendas, workshop plans, and to evaluate presentation slides before I delivered them — asking the AI to critique the flow, identify gaps, and suggest improvements the way a trusted colleague might. The difference in how I work before and after generative AI is not subtle. It is significant. And I am only just getting started with what comes next. In Article 8 we covered the four types of AI — descriptive, predictive, prescriptive, and generative — using Google Maps as our guide. We talked about how generative AI like ChatGPT and Claude can create new content — text, code, images — rather than just analysing existing data. In this article we are going deeper into two things: Generative AI — how it actually works, what it is genuinely good at, and where it still falls short Agentic AI — the next frontier, where AI does not just answer questions but takes actions and completes complex tasks on your behalf A Large Language Model — or LLM — is the technology behind ChatGPT, Claude, Gemini, and most of the generative AI tools you encounter today. Here is how to think about how it was built. Imagine reading every book, every article, every website, every piece of code ever written — billions and billions of words. As you read, you start to notice patterns. Certain words appear together. Certain sentence structures follow certain ideas. Certain concepts are always explained in similar ways. Over time you develop an incredibly deep intuition for language — not because you memorised everything, but because you absorbed the patterns. That is essentially what happens when an LLM is trained. It processes an almost incomprehensibly large amount of text and learns the statistical patterns of language — which words follow which words, which ideas connect to which concepts, how questions are typically answered. When you ask it a question it does not look up the answer in a database. It generates a response word by word, each word chosen based on what is most likely to come next given everything it has learned. This is why it feels like a natural conversation — because it is built on the patterns of natural human communication. It is also why hallucinations happen. The model is always generating what is most likely — not necessarily what is true. If the patterns in its training data point toward a plausible but incorrect answer, that is what it produces with complete confidence. Understanding this does not make LLMs less useful. It makes you a better user of them. Drafting and communication Explaining complex things simply Code review and debugging Summarising and synthesising Brainstorming and thinking partners Since we keep it honest on this blog: Hallucinations are real. AI models can generate confident, detailed, completely wrong answers. Always verify important facts, especially anything involving numbers, dates, specific technical details, or legal and medical information. Critical thinking is not optional. It does not know what it does not know. An LLM will rarely say “I have no idea.” It will generate something that sounds plausible. The less common or more specialised your question, the more carefully you need to evaluate the response. Context has limits. Most LLMs can only hold a certain amount of conversation in their working memory at once. Very long or complex projects can lose earlier context in ways that affect the quality of later responses. It has no real world awareness. A standard LLM does not know what happened yesterday, cannot access your company’s internal systems, and cannot take action in the world on your behalf. That last limitation is exactly what Agentic AI is designed to solve. Every AI tool we have discussed so far follows the same basic pattern. You ask a question. The AI answers. You ask another question. It answers again. Each interaction is a single exchange. Agentic AI breaks that pattern. An AI agent does not just answer a question — it takes a goal, breaks it down into steps, uses tools and external systems to complete those steps, learns from what it finds along the way, and delivers a completed result. Think of the difference between asking a colleague a question and delegating a task to them. Asking a question: “What flights are available from London to New York next Tuesday?” The first is what current generative AI does. The second is what agentic AI does — or is rapidly learning to do. I am currently experimenting with n8n — an open source workflow automation tool that lets you connect AI to real world actions and systems. With n8n you can build workflows where AI is not just thinking but doing. For example: Monitor your email inbox, identify action items using AI, and automatically create tasks in your project management tool Watch a folder for new documents, summarise each one with AI, and post the summary to a Slack channel Pull data from multiple sources, have AI analyse it, and generate a weekly report that gets emailed automatically Here is a simple example of what an n8n workflow looks like conceptually: Trigger: New email arrives ↓ Step 1: AI reads the email and classifies it ↓ Step 2: If it contains an action item → create a task Step 3: If it is a meeting request → check calendar and respond Step 4: If it is a document → summarise and file it ↓ Result: Inbox managed automatically

I am still in the early stages of building with n8n — it is a genuine work in progress. But even at this early stage the potential is clear. Tasks that used to require human attention for every individual item can be handled automatically, consistently, and at a scale no individual could match. It helps to think of AI capability as a spectrum rather than a binary on/off: Level 1 — AI Assistant Level 2 — AI with Tools Level 3 — AI Workflow Automation Level 4 — Fully Autonomous AI Agents We are moving along this spectrum faster than most people realise. In Cloud and Infrastructure work, agentic AI is beginning to appear in ways that will fundamentally change the role of the DevOps engineer. Automated incident response — AI agents that monitor your Kubernetes cluster, detect an anomaly, diagnose the likely cause, apply a fix, and log the resolution — without waking anyone up at 3am. Self healing infrastructure — Terraform configurations that AI can review, update, and apply in response to changing requirements or detected drift — the configuration drift problem we talked about in Article 7, solved autonomously. Intelligent CI/CD pipelines — Pipelines that do not just run fixed steps but adapt based on what they find. If a test fails, the agent analyses why, suggests a fix, and in some cases applies it automatically. AI assisted cloud cost optimisation — Agents that continuously monitor your cloud spend, identify wasteful resources, and make recommendations or take action to reduce costs without manual review. None of this is fully here yet in the way the marketing materials sometimes suggest. But the direction is clear and the pace of change is faster than anything I have seen in a decade of working in Cloud and Infrastructure. As AI becomes more capable of taking action — not just giving advice — the most important skill is not knowing how to use AI. It is knowing when to trust it, when to verify it, and when to keep a human in the loop. The engineers and professionals who will thrive in an agentic AI world are not the ones who hand everything to the AI and walk away. They are the ones who understand what the AI is doing well enough to catch it when it goes wrong and to guide it toward genuinely useful outcomes. That combination of AI capability and human judgment is going to be the most valuable professional skill of the next decade. Here is everything we covered today: Generative AI works by learning statistical patterns from vast amounts of text and generating responses word by word — which is why it is fluent but sometimes wrong It excels at drafting, explaining, summarising, code review, and thinking partnership — tools like NotebookLM make it even more powerful when grounded in your own materials Hallucinations are real — critical thinking and verification remain essential Agentic AI goes beyond answering questions to taking actions — completing multi step tasks using tools, external systems, and autonomous decision making Tools like n8n let you build agentic workflows today even as a beginner In Cloud and DevOps, agentic AI is beginning to reshape incident response, infrastructure management, and CI/CD pipelines The most valuable skill in an AI world is the combination of AI capability and human judgment We have now covered the full Pipeline & Prompts foundation — from DevOps and Linux all the way through to Generative and Agentic AI. In Article 10 we are going to zoom out and look at the big picture — how DevOps, Cloud, and AI are converging into a single discipline and what that means for anyone building a career in technology right now. We will also talk about where to go next on your learning journey — the resources, the certifications, and the hands on projects that will take you from understanding these concepts to actually building with them. The foundation is laid. Now it is time to build. See you in Article 10. Written by Pipeline & Prompts | Byte size guides on DevOps, Cloud and AI Found this useful? Share it with someone who thinks AI is just a chatbot — and show them how much further it has already come. Follow along for a new article every week.