What is Agentic AI? Important For GEN AI In 2025

Agentic AI — What it is and why it matters for GenAI in 2025

Core concept

Agentic AI = autonomous, goal-driven multi-agent systems (AI agents) that execute complex workflows without human intervention to achieve business outcomes.

• Agentic AI differs from generative AI: generative models focus on producing content from prompts; agentic systems compose, orchestrate, and execute tasks by invoking tools, APIs, and multiple models to reach objectives and adapt over time.

Key technical ideas

• Agents are independent modules that can call external tools (search, databases, finance APIs, payment gateways, etc.), other agents, and LLMs as needed. Multiple agents coordinate in a workflow to complete larger goals.
• Typical workflow pattern:
  1. Define a goal.
  2. Dispatch agents/tools.
  3. Gather data (web/news, yfinance, databases).
  4. Analyze/compare results.
  5. Return an action or recommendation.
• RAG (retrieval-augmented generation) vs agentic workflows:
  • RAG supplements LLMs with external context.
  • Agentic systems go further by autonomously choosing and running tools and sequences of actions.
• Agents can iteratively improve their own behavior during task execution (self-improvement / iterative refinement).
• Open integration across many LLM providers: OpenAI, Google Gemini, Anthropic, Mistral, Grok, AWS/Bedrock, Claude, etc.

Tools, frameworks, and product features

• Frameworks and tooling:
  • LangChain (tools concept)
  • LangGraph
  • LangFlow (no-code / drag-and-drop visual workflow design; export Python/JS; deploy endpoints)
  • Microsoft Autogen (open source)
  • F data (Fdata) — open-source framework for creating many domain agents (financial, legal, marketing, construction); integrates any LLM and many external tools/APIs
• Common tool integrations used in demos:
  • DuckDuckGo search, Wikipedia search
  • yfinance (stock data), news APIs
  • Payment gateway integrations
• Product features emphasized:
  • Multi-LLM support
  • Tool integrations and the ability to combine agents (e.g., web-search + financial agent)
  • Code export and local endpoints (demo ran on localhost:777)
  • Playground UI to test agents and quickly prototype

Demo / tutorial content (from the video)

• Live demo (F data playground) built agents:
  • Web search agent: DuckDuckGo + OpenAI chat to fetch recent news.
  • Financial agent: yfinance + OpenAI chat to fetch and analyze stock data.
  • Combined workflow: compared Tesla vs Nvidia and returned a buy/hold recommendation (example conclusion: Nvidia stronger contender; buy recommendation).
• Creator plans:
  • Convert an existing generative AI mock-interview platform into an agentic application.
  • Produce more end-to-end framework tutorials and build agentic apps in 2025, with step-by-step guides starting from basics.

Implications / industry notes

• Agentic AI is gaining traction and is expected to be a key skill area in 2025 for developers building GenAI applications.
• It may enable new classes of software—potentially competitive with traditional SaaS—by automating business workflows end-to-end.
• Many frameworks are open-source and rapidly evolving, presenting opportunities for experimentation and productization.

Main speakers / sources

• Speaker: Krish (Krish Naak) — YouTuber who presented the video and demoed the F data playground.
• Frameworks / products mentioned: LangChain, LangFlow, LangGraph, Microsoft Autogen, F data (Fdata).
• Models/providers mentioned: OpenAI, Google Gemini, Anthropic, Claude, Grok, Mistral, AWS/Bedrock.
• Tools/APIs mentioned: DuckDuckGo search, Wikipedia search, yfinance, various news APIs, payment gateway integrations.