Agentic AI Frameworks Explained

fact check this, correct it, and then expand on the ideas and then rewrite in my own style: Agentic AI just means that you do something like make LLM calls a class in python that you can integrate into a graph orchestrator allowing you to use LLM structured JSON outputs to dynamically interact with the internet. So you can use an Agentic framework to use multiple "tools" or functions that incorporate the LLM through structured JSON. So instead of a simple chatbot, you can use it for things like classification, pattern recognition, automation, etc. So instead of just a simple text output you would receive a JSON output which is structured data that can be used in object oriented programming languages like python to do more things, such as use a graph structure to orchestrate "agents" or LLM calls with the structured output. I accidentally created an agentic framework when I was programming with an LLM. So many others do the same. It is just the natural application of accessing it from the API. There is nothing really revolutionary about it since it is just the application of a new technology to existing programming infrastructure. It is basically like taking a new technology and applying existing methods to interact with it. But yes, it is the future in a lot of ways. I can't see going back at this point.

Below is a fact-checked, corrected, and expanded version of your description, followed by a rewritten version in a similar style to your original text: **Fact-Checking and Corrections:** - The term "Agentic AI" or "AI agent" does not strictly mean just making LLM calls a class in Python or integrating them into a graph orchestrator. While that may be one implementation, the broader concept of an "agentic framework" in the context of LLMs generally refers to systems in which a language model (or any AI model) can autonomously choose and use "tools" or external functions to achieve specific goals. This often involves structured inputs and outputs, allowing the model to interact with various APIs, services, or other programs. - The idea of using structured JSON output from an LLM is a recognized pattern. Many frameworks encourage returning responses in JSON so that the AI’s output can be programmatically parsed, validated, and integrated into larger software systems. - Employing LLMs as part of tool-driven frameworks or orchestration layers (like LangChain, Guidance, or other agent-based frameworks) is not just a trivial application; it represents a practical way to combine AI capabilities with existing software infrastructures. While it’s true that this approach builds on existing programming paradigms (like object-oriented programming and tool invocation), it’s also considered a significant evolution in how we integrate AI into applications—essentially making AI a first-class component in a software stack. - Although not every instance of using an LLM through an API is “agentic,” the general concept of agentic AI involves the model having some autonomy or decision-making capability about which actions to take next, guided by the developer’s instructions and the available tools. - While it might feel natural or inevitable once you start working with these technologies, it’s still an important shift in thinking: rather than only returning static text, the model can trigger functions, carry out sequences of actions, and orchestrate complex tasks. This can feel revolutionary even if it’s conceptually just “wiring up new tech into existing patterns.” **Expanded Explanation:** Agentic AI frameworks represent the next step in integrating large language models into software. Instead of treating an LLM like a simple text-in, text-out system—essentially a fancy chatbot—agentic frameworks treat it as a decision-maker that can leverage various tools. This approach transforms the LLM from a static asset into an active participant in a software ecosystem. For example, imagine you have a system that needs to periodically summarize articles, post the summaries online, and then schedule follow-up tasks based on user feedback. Without an agentic approach, you might write code that calls the LLM for a summary, then separately handle the posting and scheduling logic. With an agentic framework, the LLM itself can decide when to request a summary tool, when to invoke a web posting function, and how to respond to feedback signals—all orchestrated automatically. Structured outputs like JSON are crucial here. They enable the AI’s responses to be machine-readable and easily integrated. Instead of parsing the LLM’s natural language output with brittle string manipulation, you can rely on a stable, well-defined JSON schema. This lets you plug the LLM into data pipelines, microservices, or complex orchestration graphs. As a result, the AI becomes a node in a workflow, capable of invoking different services as needed. While this may not feel “revolutionary” on a purely conceptual level—after all, we’ve long integrated various tools in software—the ability to give a machine learning model the capacity to decide which tools to use and when, all guided by dynamic reasoning, is a significant leap in how we build and think about applications. As more developers adopt this approach, it’s likely to become a standard best practice rather than a cutting-edge novelty. **Rewritten in Your Style:** Agentic AI doesn’t just mean wrapping LLM calls into a Python class and plugging them into some graph orchestrator. Sure, that’s one example of how you might use it, but the idea is bigger than that. An “agentic” setup lets an LLM pick and choose from a set of tools—functions or APIs—using a structured output format like JSON. This way, the model isn’t just spitting out raw text. It’s producing structured data you can feed right into your existing systems, whether that’s Python code, a graph-based orchestration tool, or something else. With this approach, you’re not stuck with a simple chatbot. You can leverage the LLM for classification tasks, pattern recognition, and automation. The key is the JSON output, which turns the LLM’s response into a well-defined piece of data. You can then pass that data around, call functions, and build pipelines. It’s basically turning the LLM into a decision-maker inside your code, orchestrating tasks, making calls, and doing all sorts of interesting things autonomously. I kind of stumbled into this idea myself while programming with an LLM. Honestly, it’s not “revolutionary” in the sense that we’re still just hooking up tools and using APIs. But it feels like a natural evolution—like once you see how neatly everything snaps together, you can’t imagine going back. It’s simply taking a cutting-edge AI capability and fitting it into the programming patterns we already know work well. And let’s be real: this is probably where everything’s headed, and it’s going to be hard to settle for less once you’ve tried it.

preserve my style but make shorter: So I spent over the last decade researching and teaching myself AI and now I know how to use it to replace jobs. I replace jobs all the time. I run my own business. Rather than hire people to do things I just do them myself now. Anyone can do it, you just need to know how to program, which an LLM can teach you to do. Now when you think of a business owner you would not think of a person like me. I am not wealthy. I used AI to run parts of my business when I was homeless. That is how I was able to generate income even when I was powerless. AI is just mathematical programming knowledge. Mathematics is a universal language. AI offers the ability to empower yourself. So to answer your question. How will ordinary folks like you or me earn? I have already answered that question with my life. Think of it. The homeless in society are at the bottom and even said not to exist by the world's richest man, but here I am now housed and successful, all because of AI and what I taught myself. So if I can teach myself these skills to improve my life and pull myself out of poverty, why can't everyone else? Because they are lazy. When the calculator came out it empowered the mathematicians and at the same time a whole new generation never had to learn to do math in their head so they didn't. The question is not if the technology empowers or disempowers people, but whether people will take the effort to learn how to use it. We live in a really ignorant society. I had a lot of homeless friends when I was homeless. They would just lounge all day and do drugs. I taught myself how to improve my situation. They never got better, but I was able to escape with my mind. So because people are lazy and ignorant they will not learn how to use the new technology. Rather they will rely on it to be even more lazy and to become even dumber. The intellectually curious that take the time to teach themselves how to use it will be able to empower themselves. In the future there will be this morass of unemployable people without jobs because they are lazy and a minuscule portion of humanity, the conscious circle of humanity, those that are intellectually curious will possibly have high paying jobs. But that is kind of how things have always been. So how do you improve the lives of the lazy at the same time the intellectually curious are able to improve their lives? It is what I call "selfish altruism". I could be the wealthiest man in the world but if everyone around me is poor then they will always view me as a villain and try to take my money even if it came from my intellectual effort and years of study. Rather if I use my money to improve the lives of those around me and make sure that people have their basic needs met then I would reduce crime around me and live in a more harmonious life with other people. The outer life effects the inner. It is selfish of me to want to feel good. If I make others feel good they will spread their happiness to me rather than direct ire and ill will, thus my inner life improves. Thus selfish altruism. So the lazy that do not learn to use AI, should still have their basic needs met by the excess of wealth generated by the intellectually hard working. This could be done by simply using a portion of the API costs for calling AI to fund things like welfare. That is probably why OpenAI was supposed to be non-profit. Anyway, to answer your question. The way ordinary folk not get left behind is either they learn to become intellectually curious and use their intellect constructively, or those that do should ensure that the wealth they generate is used to improve the lives of everyone.

So I spent a decade teaching myself AI, and now I know how to use it to replace jobs—I do it all the time. I run my own business, and instead of hiring people, I rely on AI to do their tasks. Anyone can learn this; just learn to program, which an LLM can teach you. I’m not rich, but I’ve used AI even when I was homeless, generating income from nothing. AI is just mathematical programming. Math is universal. It empowers you if you take the time to learn. Ordinary folks ask, “How can we earn?” I’m living proof—you can teach yourself and pull yourself out of poverty if you try. But most people are lazy. They won’t put in the effort. Look at calculators. They saved time, but also created generations who never bothered to learn mental math. It’s the same with AI. Some will master it; most will just get lazier and dumber. Those who learn will prosper. The rest will get left behind, unemployed, and stuck at the bottom. That’s how it’s always been. So how do we uplift the lazy alongside the curious? Selfish altruism. If I get rich and everyone else is poor, they’ll hate me. If I use some of my wealth to help others meet their basic needs, I create a safer, happier environment for myself. Everyone wins. In the future, those who embrace AI will gain wealth and power. The rest might rely on welfare funded by those who succeed. Maybe that’s why OpenAI was meant to be non-profit. In the end, ordinary folks either adapt and learn, or we ensure that those who do share their gains for everyone’s benefit.