What Is “Agentic AI”? (And Why It’s Not Black Magic)
The term agentic AI sounds like something revolutionary – an AI agent acting with autonomy, maybe plotting your schedule or launching rockets while you sleep. In early 2023, projects like AutoGPT and BabyAGI burst onto the scene, promising AI agents that could plan and execute multi-step tasks with minimal human intervention. Enthusiasts imagined a digital butler or junior executive that could take a goal (“Research my competition and draft a strategy”) and handle all the busywork autonomously. It captured imaginations (AutoGPT’s repo hit 100k stars in months), but also led to inflated expectations. So what is an agentic AI, really?
At its core, an AI agent is just an application structure where a large language model (LLM) – think GPT-4 or similar – serves as the reasoning engine. The LLM is given a goal and some instructions, and it decides what to do next, in a loop of “Thought → Action → Feedback”. In other words, the AI thinks about the problem, outputs an action command (for example, “search the web for X” or “summarize this document”), then some program executes that action, and the result is fed back into the AI for the next cycle. The agent keeps looping like this until it reaches the goal or runs out of steps. It’s essentially a programmable workflow wrapped around an LLM, not an omniscient digital brain. As one analysis bluntly put it, these systems raise the question of whether they are truly autonomous or “just clever wrappers around existing LLMs.” In practice, it’s more the latter.
Think of an agentic AI as an eager intern with a touch of amnesia. You give this intern (the LLM) a mission and a list of tools it’s allowed to use. It can browse websites, call APIs, run calculations – whatever tools you’ve programmed in. The intern will figure out a plan step by step, ask to use a tool (“I should Google the client’s name”), get the result, and then figure out the next step, looping until the task is done. Importantly, the intern isn’t magic – it only knows what it’s been taught and what you tell it. Each action is prompted by the previous result and the overarching goal you set. Under the hood, there’s code orchestrating this loop, managing the prompts and responses, and executing those tool calls. In the end, as developer Cornelia Davis notes, an AI agent is an event loop with an LLM in the middle – “not as magical (or as mysterious) as it might seem initially.”
Early agentic AI frameworks like AutoGPT made this concrete. AutoGPT would spin up GPT-4 with a system prompt telling it to behave as an autonomous agent, and a user prompt describing the goal. On each loop, the model was expected to output a plan, a reasoning trace, and an action command (say, “Open browser and search for market trends”). AutoGPT’s code would execute that command (perform the search), then feed the results (the search findings) back into the next prompt. AutoGPT was breaking down a large task into several smaller tasks then acting as an orchestrator to achieve the initial goal. This pattern – LLM thinks, tool acts, repeat – is what defines agentic AI. It’s powerful in that the AI can handle unstructured tasks and adapt on the fly. But it’s not a sorcerer. It’s constrained by the tools you give it, the quality of its prompts, and the fundamental limits of the LLM’s knowledge.
Agentic AI often works best when combined with traditional programming. One guide describes it well: “Agentic AI combines the best of both worlds, using LLMs for more creative and flexible undertakings, and programming for rules-based, high-performance tasks.” The AI provides intuition and adaptability; the code provides precision and reliability. Together, you get an autonomous-seeming system that can handle tasks like a human (e.g. parsing an email and drafting a reply in natural language) while still interfacing with databases, APIs, or hardware in a controlled, deterministic way. But everything the agent does is because a human engineered it to work that way. Someone wrote the prompt templates, coded the tool interfaces, and set the boundaries. Agentic AI is an engineered illusion of autonomy and it's still very useful.
Meet n8n: Automation for the Rest of Us
On the other sits n8n. n8n is a general-purpose no-code/low-code workflow automation tool that lets you connect different apps and services to automate tasks, similar to Zapier or Microsoft’s Power Automate. If agentic AI is like our improvising intern, n8n is more like a very diligent clerk with a checklist: it will do exactly what you’ve drawn out, every time, reliably. With n8n, you create flows by dragging and dropping nodes on a canvas. Each node might be a trigger (say, “New email received” or “Cron job fires at 7:00 AM”) or an action (“Send a Slack message”, “Call this API”, “Run this code snippet”). You connect nodes together, mapping out the path of a task from start to finish. When the trigger fires, the workflow runs through the nodes in sequence. No surprises, no deviations, unless you explicitly add conditional branches.
For example, imagine you want to automate a daily report. In n8n, you might set up: a Cron node to trigger at 6 AM every day, then a database query node to fetch yesterday’s sales, then a LLM node to summarize those numbers in plain English, and finally an email node to send that summary to your team. This chain is visual and easy to follow. N8n takes care of the plumbing – underneath the canvas it’s executing each step and passing the data along – but you, the user, decide exactly what the workflow does. It’s automation by design, not by on-the-fly reasoning. Crucially, it requires no coding for most tasks. This approach makes n8n popular among indie developers and startup tinkerers who need quick solutions without spinning up a whole software project. It’s free, open-source, and you can self-host it or use a cloud version, lowering the barrier to entry for robust automation.
Now, n8n has recently embraced AI as well. It provides built-in nodes for AI services: you can drop in an OpenAI GPT-4 node, for instance, to generate text, or even use a specialized “AI Agent” node via LangChain integration. In plain terms, this means you can build agentic behavior within an n8n workflow. You could have a chat conversation node where an LLM “decides” something, and then based on that decision, different branches of the workflow execute. It’s still you designing the overall logic on the canvas, but the AI node can introduce some dynamic decision-making. For example, your n8n flow might say: if a support ticket arrives, use an AI node to determine whether it’s high priority; the AI’s output (say it says “urgent” or “normal”) routes the ticket to the appropriate queue via different nodes. The AI adds adaptability to an otherwise static flow.
However, n8n’s simplicity is also its constraint. If your needs grow very complex – lots of conditional branches, looping, memory, custom logic – you might find a sprawling, tangled mess on the canvas. There’s a point where adding one more edge-case branch in n8n becomes more cumbersome than just writing code. As one expert noted, if an n8n automation gets very intricate, “at some point you are configuring so many nodes that writing code might be easier.” This isn’t a criticism of n8n; it's designed this way. Low-code tools excel at getting something working fast. But they sacrifice the fine-grained control and efficiency you’d have hand-coding the solution in a language like Python or C++. N8n is an easier, more accessible way to build workflows, but ultimately less customizable than an engineered-from-scratch programmatic solution. It’s the difference between assembling IKEA furniture (quick, with pre-made parts) and carpentry from raw lumber (slower but infinitely flexible). If you have a straightforward task or a moderate-complexity workflow, n8n works extremely well. For instance, you can connect Gmail to Excel to Slack in minutes. If you have a highly specific or complex process (say a custom AI model that needs to loop over data with nuanced state), you might outgrow n8n and drop down to code, or use n8n’s Code nodes to inject custom scripts where needed.
Importantly, n8n isn’t the only solution in this space. Its philosophy of “workflow automation for everyone” is something big tech has also embraced. Microsoft, for instance, offers Power Automate (formerly Microsoft Flow) as part of the Office 365 suite – a very similar drag-and-drop workflow builder that connects your Outlook, SharePoint, Teams, etc. In fact, Microsoft is now blending AI into those workflows too. In 2024 they announced “AI flows” in Power Automate: instead of manually mapping out every step, you can simply describe the objective in natural language and let an LLM propose an automation plan. The AI will actually reason over what actions are needed and in what sequence to achieve your goal, then set up the flow. It's doing what an AI agent would do – “You want to process invoices? Okay, I (the AI) will choose to use the PDF reader, then the OCR, then the Excel connector to log the data” – but in the familiar confines of a workflow tool. The bottom line: whether it’s n8n, Power Automate, Zapier, or others, the trend is converging. Automation is becoming easier to create, and AI is being injected into it to make it more powerful. But we’re still fundamentally just automating workflows.
Sources
AutoGPT GitHub Repository https://github.com/Significant-Gravitas/AutoGPT
Cornelia Davis, “Making Friends with Agents: A Mental Model for Agentic AI Applications” https://temporal.io/blog/a-mental-model-for-agentic-ai-applications
n8n Documentation https://docs.n8n.io/
Microsoft Power Automate AI Flows Announcement https://www.microsoft.com/en-us/power-platform/blog/2024/05/21/revolutionize-the-way-you-work-with-automation-and-ai/
Dynatrace Blog, “Agentic AI: Model Context Protocol, A2A, and automation’s future” https://www.dynatrace.com/news/blog/agentic-ai-how-mcp-and-ai-agents-drive-the-latest-automation-revolution/
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