Listen to this Post
Language models (LLMs) revolutionized AI by predicting text sequences, while Retrieval-Augmented Generation (RAG) enhanced responses using external knowledge. Now, Agentic AI represents the next leap—autonomous systems that plan, learn, and act independently.
Why Agentic AI is a Breakthrough
- Remembers: Retains past interactions for continuous improvement.
- Strategizes: Breaks tasks into steps and adapts dynamically.
- Acts: Integrates APIs/tools to execute real-world tasks.
- Achieves: Delivers results, not just answers.
🔗 WhatsApp Channel for AI/Data Science Resources: AI Ka Doctor
You Should Know: Practical AI/IT Implementations
1. Running LLMs Locally (Linux/Windows)
Deploy open-source LLMs like Llama 2 or Mistral using:
Install Ollama (Linux/macOS) curl -fsSL https://ollama.ai/install.sh | sh ollama pull llama2 ollama run llama2
For Windows (WSL):
wsl --install wsl curl -fsSL https://ollama.ai/install.sh | sh
2. Building a RAG System
Use LangChain + FAISS for document retrieval:
from langchain.document_loaders import WebBaseLoader
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
loader = WebBaseLoader("https://example.com")
docs = loader.load()
embeddings = HuggingFaceEmbeddings()
db = FAISS.from_documents(docs, embeddings)
3. Agentic AI with AutoGPT
git clone https://github.com/Significant-Gravitas/Auto-GPT cd Auto-GPT pip install -r requirements.txt cp .env.template .env Edit .env with OpenAI API key python -m autogpt
4. Monitoring AI Agents
Use Prometheus + Grafana for metrics:
Install Prometheus (Linux) wget https://github.com/prometheus/prometheus/releases/download/v2.30.3/prometheus-2.30.3.linux-amd64.tar.gz tar xvfz prometheus-.tar.gz cd prometheus- ./prometheus --config.file=prometheus.yml
What Undercode Say
Agentic AI marks the shift from reactive to proactive systems. Key takeaways:
– LLMs → RAG → Agents = Evolution from text prediction to autonomous problem-solving.
– Ethical risks: Autonomous agents require guardrails (e.g., AI alignment frameworks).
– Future: Self-improving AI ecosystems will dominate cybersecurity, DevOps, and decision-making.
Expected Output:
- Autonomous AI agents executing tasks (e.g., bug fixes, cloud deployments). - Increased demand for AI auditing tools (e.g., TensorFlow Privacy). - Convergence of AIOps and MLOps for self-healing systems.
Prediction: By 2026, 40% of enterprise workflows will integrate Agentic AI for automation, reducing human intervention in IT operations.
🔗 Relevant Course: Advanced AI Agents on Coursera
IT/Security Reporter URL:
Reported By: Habib Shaikh – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
