Merge pull request #3098 from dineshr1493/cms/dineshr1493/hpe-dev-portal/blog/from-generative-to-agentic-ai-—-part-2-what-makes-ai-agents-truly-intelligent

Create Blog “from-generative-to-agentic-ai-—-part-2-what-makes-ai-agents-truly-intelligent”

Author: choukden

content/blog/from-generative-to-agentic-ai-—-part-2-what-makes-ai-agents-truly-intelligent.md

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+---
+title: "Part 2: What makes AI agents truly intelligent"
+date: 2025-07-09T04:15:26.181Z
+author: DINESH R SINGH
+authorimage: /img/dinesh-192-192.jpg
+disable: false
+tags:
+  - "AI Agents "
+  - Agentic Frameworks
+  - Agno
+  - Autogen
+  - Langchain
+  - Enterprise Agents
+---
+<style>
+li {
+   font-size: 27px;
+   line-height: 33px;
+   max-width: none;
+}
+</style>
+
+In the [first part of this series](https://developer.hpe.com/blog/from-generative-to-agentic-ai-tracing-the-leap-from-words-to-actions/), I discussed the shift from passive large language models to more capable, action-oriented AI. Now, I will provide a closer look at what actually powers this transformation — the concept of the AI agent. Far from being just an advanced chatbot, an agent is a structured system that can understand, plan, execute, and respond — much like a real-world assistant, only faster and smarter.
+
+Inspired by [my post on Medium](https://dineshr1493.medium.com/all-you-need-to-know-about-the-evolution-of-generative-ai-to-agentic-ai-part-2-agentic-ai-74dcf045aff0), this post builds upon the original work with added clarity, practical examples, and a more conversational tone to help you truly grasp how agentic AI is reshaping automation across industries.
+
+<center><img src="/img/screenshot-2025-07-18-at-4.10.42 pm.png" width="500" height="542" alt="AI agnet Framework" title="AI agnet Framework"></center>
+
+## What are AI agents?
+
+An AI agent is not just something that responds to prompts — it’s something that takes initiative. Unlike traditional LLMs, which generate output only when asked, agents can independently decide what actions to take, how to take them, and when to stop.
+
+Here’s what makes an agent different:
+
+* It can decide how to solve a problem based on context.
+* It can use tools such as APIs, search engines, or databases.
+* It can take real actions, like analyzing data, sending emails, or making reservations.
+* It can break down large tasks into smaller, manageable steps and complete them autonomously.
+
+In essence, an AI agent behaves more like a virtual assistant capable of doing actual work — not just holding a conversation.
+
+## The agent workflow: Think → Plan → Act → Respond
+
+The heart of an agentic system lies in this continuous loop:
+
+* **Think :** It starts with understanding the objective or problem at hand.
+* **Plan :** Based on that understanding, it creates a strategy — often a sequence of steps.
+* **Act :** It then begins executing the plan, calling tools, retrieving data, or initiating actions.
+* **Respond :** Finally, it summarizes or communicates the results — or loops back to continue solving.
+
+This cycle allows agents to operate with minimal human intervention, even on complex, multi-step workflows.
+
+## Real-world impact: How agents are already changing industries
+
+**Healthcare:** AI agents can retrieve patient history, summarize medical notes, and monitor for critical conditions, aiding doctors in real-time.
+
+**Finance:** Agents can analyze markets, detect fraud, and automate reporting — operating at speeds no human team could match.
+
+**Customer support**: Instead of generic replies, agents can pull data from CRM systems, open service tickets, or resolve technical issues directly.
+
+**IT and DevOps:** Agents are now monitoring systems, fixing bugs, deploying updates — all without waiting on a human operator.
+
+These are not theoretical applications — they are happening right now, streamlining operations and reducing bottlenecks across the board.
+
+## Agents + Tools = Real-world superpowers
+
+What truly empowers agents is their ability to interface with tools. Think of APIs, web services, internal databases, scripts, and even IoT systems. These integrations allow agents to interact with the real world, not just the digital one.
+
+For example:
+
+* Google Maps or Search APIs.
+* CRM and ERP databases.
+* Automation scripts for cloud platforms or internal workflows.
+
+When agents are connected to tools, they don’t just think — they execute.
+
+## Need for AI agents
+
+The value of AI agents lies in their ability to scale thinking into action:
+
+* They automate entire workflows — not just single responses.
+* They handle decision-making on the fly.
+* They adapt to changing inputs and data.
+* They reduce repetitive manual work across industries.
+* They can collaborate as multi-agent teams to solve broader, interconnected problems.
+
+## Top 5 frameworks to build AI agents
+
+If you're ready to build with agents, here are the top frameworks that developers swear by:
+
+* Auto-GPT
+* BabyAGI
+* AGNO
+* LangChain
+* Autogen by Microsoft
+
+| Framework             | What it does                                       | GitHub Link                                                   | ⭐ Stars |
+| --------------------- | -------------------------------------------------- | ------------------------------------------------------------- | ------- |
+| **AGNO (Phidata)**    | Build fast, multi-modal agents (text + images)     | [AGNO GitHub](https://github.com/phidatahq/phidata)           | ⭐ 21.5k |
+| **Auto-GPT**          | The first viral agent that automates tasks         | [Auto-GPT GitHub](https://github.com/Torantulino/Auto-GPT)    | ⭐ 174k  |
+| **BabyAGI**           | Agents that manage task lists autonomously         | [BabyAGI GitHub](https://github.com/yoheinakajima/babyagi)    | ⭐ 21.2k |
+| **LangChain Agents**  | Powerful and flexible agent toolkit for developers | [LangChain GitHub](https://github.com/langchain-ai/langchain) | ⭐ 104k  |
+| **Microsoft Autogen** | Build multi-agent systems that work together       | [Autogen GitHub](https://github.com/microsoft/autogen)        | ⭐ 42k   |
+
+Each offers a different approach — some focus on chaining tasks, others on autonomy and memory. Together, they make it easier than ever to bring agentic AI to life.
+
+## **Conclusion**
+
+AI agents are no longer a futuristic idea — they’re here, and they’re transforming how work gets done. By combining decision-making, planning, and tool usage, agents represent the leap from intelligent text generation to intelligent action. They’re bridging the gap between knowing what needs to be done and actually doing it.
+
+[In Part 3 of this series,](https://developer.hpe.com/blog/model-context-protocol-mcp-the-protocol-that-powers-ai-agents/) I'll dig deeper into the architecture behind agentic systems — what components make them tick, how memory and feedback loops work, and how they can scale. If you're building the future or just trying to understand it, you're in the right place.
+
+Until then, keep watching the space where AI stops being a helper… and becomes a doer