Listen to this Post
Agentic AI frameworks are revolutionizing automation by enabling intelligent, autonomous workflows. Below are the top frameworks reshaping industries:
🔷 AutoGPT
Executes complex tasks autonomously with minimal user intervention.
🔷 LangChain
Chains tasks together, leveraging external data sources for richer automation.
🔷 BabyAGI
Automates task creation and execution in a continuous loop.
🔷 Microsoft Autogen
Enables multi-agent collaboration for advanced workflows.
🔷 Hugging Face
Empowers NLP task automation at scale.
🔷 Camel
Focuses on efficient multi-agent problem-solving.
🔷 Haystack
Builds search-centric agents for powerful Q&A solutions.
🔷 LlamaIndex
Connects LLMs to structured data for knowledge-based responses.
🔷 CrewAI
Orchestrates multi-agent collaborations seamlessly.
🔷 Rasa
Open-source platform for building conversational AI and chatbots.
You Should Know:
Practical AI Automation with LangChain
LangChain allows seamless integration of LLMs with external data. Here’s a basic setup:
from langchain.llms import OpenAI from langchain.chains import LLMChain llm = OpenAI(model_name="gpt-4") prompt = "Explain how Agentic AI works." chain = LLMChain(llm=llm, prompt=prompt) print(chain.run())
Running AutoGPT Locally
Install AutoGPT and run autonomous tasks:
git clone https://github.com/Significant-Gravitas/AutoGPT.git cd AutoGPT pip install -r requirements.txt python -m autogpt --gpt3only --continuous
BabyAGI Task Automation
Deploy BabyAGI for recursive task execution:
from babyagi import BabyAGI agent = BabyAGI(objective="Research AI trends") agent.run()
Microsoft Autogen Multi-Agent Setup
Configure multiple agents for collaborative workflows:
from autogen import AssistantAgent, UserProxyAgent
assistant = AssistantAgent("assistant")
user_proxy = UserProxyAgent("user_proxy")
user_proxy.initiate_chat(assistant, message="Plan a cybersecurity strategy.")
Hugging Face NLP Pipeline
Run NLP tasks with Hugging Face Transformers:
from transformers import pipeline
nlp = pipeline("text-generation", model="gpt-3")
print(nlp("Explain AI agent frameworks."))
Haystack Q&A System
Build a search-based Q&A agent:
from haystack.document_stores import InMemoryDocumentStore from haystack.nodes import FARMReader document_store = InMemoryDocumentStore() reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2")
Rasa Chatbot Deployment
Deploy a Rasa-based AI chatbot:
pip install rasa rasa init --no-prompt rasa train rasa shell
What Undercode Say:
Agentic AI frameworks are pushing automation beyond scripted responses into dynamic decision-making. Enterprises adopting these tools will lead in efficiency, while those ignoring them risk falling behind. Expect tighter integration between AI agents and cybersecurity tools, with autonomous threat response becoming mainstream.
Expected Output:
AI agents executing tasks autonomously, integrating with enterprise systems, and enhancing cybersecurity defenses.
Prediction:
By 2026, 60% of enterprise workflows will incorporate Agentic AI, reducing manual intervention by 40%. Multi-agent collaboration will dominate complex problem-solving in IT, cybersecurity, and cloud automation.
Relevant URLs:
IT/Security Reporter URL:
Reported By: Quantumedgex Llc – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
