Merge pull request #3092 from dineshr1493/cms/dineshr1493/hpe-dev-portal/blog/from-generative-to-agentic-ai-tracing-the-leap-from-words-to-actions

Create Blog “from-generative-to-agentic-ai-tracing-the-leap-from-words-to-actions”

Author: choukden

content/blog/from-generative-to-agentic-ai-tracing-the-leap-from-words-to-actions.md

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+---
+title: "From generative to agentic AI: Tracing the leap from words to actions"
+date: 2025-07-03T10:33:21.361Z
+author: DINESH R SINGH
+authorimage: /img/dinesh-192x192.jpg
+disable: false
+tags:
+  - Agentic AI
+  - Gen AI
+  - LLM
+---
+<style>
+li {
+   font-size: 27px;
+   line-height: 33px;
+   max-width: none;
+}
+</style>
+
+AI has come a long way from simply finishing our sentences. Today, it’s not just generating content — it’s actively solving problems, making decisions, and executing complex tasks. This blog post kicks off a 10-part series where I'II trace that incredible journey — from basic generative models to fully autonomous agents. Along the way, I’ll unpack the key shifts, architectures, and mindsets that shaped this evolution.
+
+Inspired by a [my post on ](https://dineshr1493.medium.com/all-you-need-to-know-about-the-evolution-of-generative-ai-to-agentic-ai-65de72254a86)[Medium](https://dineshr1493.medium.com/all-you-need-to-know-about-the-evolution-of-generative-ai-to-agentic-ai-65de72254a86)[,](https://dineshr1493.medium.com/all-you-need-to-know-about-the-evolution-of-generative-ai-to-agentic-ai-65de72254a86) this piece reimagines and expands on the original with a human-first lens and practical clarity.
+
+Whether you're an AI developer, tech leader, or just curious about where all this is headed — welcome. Let’s dive in.
+
+## Phase 1: LLMs — The linguistic powerhouse
+
+Large Language Models (LLMs) like GPT, DeepSeek, QWEN, and LLaMA burst onto the scene with one incredible skill — understanding and generating human language. These models are trained on massive datasets and excel at:
+
+* Multilingual conversations
+* Summarization, classification, and text generation
+* Contextual prediction based on vast patterns
+
+But here’s the catch:
+
+LLMs are great at “saying” things… but they don’t do anything.
+
+On their own, LLMs are like brilliant thinkers without hands — capable of deep analysis, but unable to act in the real world.
+
+<center><img src="/img/llms.png" width="600" height="550" alt="LLM Evolution" title="LLM Evolution"></center>
+
+## Phase 2: LLMs + Tools — Giving the brain some hands
+
+The next leap came when developers began connecting LLMs with external tools — APIs, plugins, databases, and custom workflows. This simple but powerful integration gave models the ability to:
+
+* Search the web (like Perplexity AI)
+* Execute code and commands
+* Fetch real-time or contextual information
+
+This expanded what AI could do. Suddenly, the models weren’t just conversational — they became useful assistants.
+
+**But there was still a problem:**
+
+Tool-based systems are fragile. APIs break, schemas change, and workflows can become unreliable.
+
+Think of it like giving a brain a set of hands — but the hands don’t always listen, or worse, they change shape every other week.
+
+## Phase 3: LLMs + Agents — The rise of agentic AI
+
+This is where things get truly exciting.
+
+Agentic AI introduces a new layer of intelligence: autonomy. Instead of the model responding directly to every input, agentic systems:
+
+* Set goals
+* Break them into tasks
+* Select and operate tools
+* Make iterative decisions
+* Learn from outcomes
+
+In essence, AI stops being reactive and starts becoming proactive. These agents operate like digital coordinators — orchestrating actions, delegating responsibilities, and adjusting course as needed. They move beyond simple tasks and begin solving complex workflows.
+
+This isn’t just a better assistant — it’s the early form of AI co-workers.
+
+**TL;DR Breakdown**
+
+* LLMs = Great with words, but passive
+* LLMs + Tools = Adds capabilities, but brittle and manual
+* LLMs + Agents = Autonomous systems that think, plan, and act
+
+**We’ve moved from “talking AI” to “doing AI.”**
+
+## Conclusion
+
+The shift from generative to agentic AI is more than just a technical upgrade — it’s a philosophical turning point in how we think about artificial intelligence. We’re no longer training machines to just converse with us; we’re teaching them to collaborate, adapt, and even take initiative. Agentic AI is the foundation for everything from self-operating software agents to autonomous business logic.
+
+In the next part of this series, I’ll peel back the curtain on how agentic architectures actually work — the brains behind the autonomy. Until then, consider this: the next time you interact with an AI, it may not just be listening… it may already be planning your next move.