What is Agentic AI? Important For GEN AI In 2025

Agentic AI is positioned as the next step beyond generative AI: instead of producing text as the end goal, autonomous AI agents pursue a defined business outcome by running multi-step workflows, calling external tools, and coordinating with other agents. That shift matters because it turns LLMs from “content generators” into systems that can retrieve up-to-date information, make decisions, and execute tasks with minimal human intervention—capabilities that are increasingly central to real-world applications in 2025.

In generative AI, an LLM typically responds to a user prompt by generating content. The workflow is straightforward: a user asks a question, a prompt instructs the model how to behave (for example, “act like an author” or “write a poem”), and the model outputs text. Even when generative AI is paired with retrieval approaches like RAG, the model still isn’t inherently equipped with the latest internet data; it relies on external retrieval or tools to fill the gap. Frameworks such as LangChain help by providing “tools” (e.g., a DuckDuckGo search tool or a Wikipedia search tool) that the model can call to fetch information, but the overall aim remains content generation.

Agentic AI changes the objective and the structure. The core idea is an autonomous AI system with a specific goal. To reach that goal, the system can operate independently—assembling and executing complex workflows that may require multiple tools and multiple sub-agents. Rather than manually stitching together one tool after another for each request, agentic systems can plan, call the right tools, and iterate until the outcome is achieved. The workflow can also improve over time, with the system adjusting to perform better as tasks complete.

A concrete example illustrates the difference. Consider a custom finance bot tasked with deciding whether to buy Tesla or Nvidia, with constraints like buying stocks, selling in five days, and targeting a minimum profit threshold. In an agentic setup, one agent can pull stock details via a tool such as YFinance, while another agent can fetch recent finance news and sentiment through external news APIs. These agents then work together in a coordinated workflow: compare stock metrics, incorporate recent developments, and produce a recommendation. That kind of end-to-end decision support is framed as difficult for purely generative AI because generative systems mainly generate responses rather than autonomously orchestrating tool-driven steps toward a business outcome.

The transcript also highlights several frameworks for building agentic applications. F data is presented as an open-source option for creating multiple specialized agents (e.g., legal, financial analysis, construction analyst, marketing agent) and integrating with a wide range of LLM providers (from Grok and Google Gemini to Anthropic, AWS, OpenAI, and Mistral) plus arbitrary tools and external systems. Microsoft AutoGen is described as another open-source framework for agentic AI. LangFlow is positioned as a no-code, drag-and-drop way to design workflows and generate deployable code. LangGraph is emphasized for building complex, multi-agent workflows and supporting multiple models and preprocessing/prompting steps.

Finally, an end-to-end demo uses F data to create a “web search agent” that interacts with third-party sources for recent news, paired with a “financial agent” that uses YFinance. Combined in a playground, the system answers questions like which stock to buy between Tesla and Nvidia by internally invoking agents, pulling tool data, and returning a structured recommendation.