Author(s): Rashmi Originally published on Towards AI. How to move from “chatting with an AI” to engineering reliable, production-grade agent systems with Claude Code. How to move from “chatting with an AI” to engineering reliable, production-grade agent systems with Claude Code. The …

Author(s): Rashmi Originally published on Towards AI. Subtitle AutoGen is Microsoft’s open-source framework for building multi-agent AI applications where multiple AI agents collaborate to solve complex tasks. It enables agents to have conversations with each other, share context, and work together autonomously …

Author(s): Rashmi Originally published on Towards AI. Persistence in LangGraph — Deep, Practical Guide Persistence in LangGraph means storing and restoring graph state so an agent/workflow can: Image description not provided in the HTMLThe article explores the importance of persistence in LangGraph, …

Author(s): Rashmi Originally published on Towards AI. Why MCP Matters: A Deep Dive into Model Context Protocol MCP = a standard protocol that lets AI apps/agents connect to external tools + data sources in a consistent way. Instead of building a custom …

Author(s): Rashmi Originally published on Towards AI. Kafka vs Kinesis (2026): A Practical Guide to Streaming, Use Cases, Architecture, and Code Event streaming is the nervous system of modern data platforms: clickstreams, payments, logs, IoT telemetry, fraud signals, and ML features all …

Author(s): Rashmi Originally published on Towards AI. MLflow vs Kubeflow vs Airflow: Choosing the Right MLOps Tool for Real-World Production Systems Machine Learning models rarely fail because of algorithms. They fail because pipelines break, experiments are lost, deployments drift, and nobody knows …

Author(s): Rashmi Originally published on Towards AI. Agent Lightning: From Agent Experiments to Self-Improving AI Systems Agent Lightning is a training and optimization runtime for agents. Agent Lightning is an innovative, research-driven, open-source initiative by Microsoft that focuses on transforming agent executions …

Author(s): Rashmi Originally published on Towards AI. CNN vs RNN: Two Brains of Deep Learning Convolutional Neural Network (CNN) is a specialized deep learning architecture designed to process grid-like topology data, primarily images, by automatically learning spatial hierarchies of features through backpropagation. …

Author(s): Rashmi Originally published on Towards AI. Inside Latent Space: The Hidden Intelligence of AI Systems Latent space is the compressed “meaning space” where AI models transform messy real-world inputs (text, images, audio, sensor signals) into dense vectors (embeddings) that capture patterns, …

Author(s): Rashmi Originally published on Towards AI. Adversarial NLP in 2026: When Text Attacks Text Adversarial NLP is the study and practice of crafting text inputs that cause NLP systems to behave incorrectly — misclassify, leak secrets, follow malicious instructions, or take …

Author(s): Rashmi Originally published on Towards AI. LSTM vs GRU: Architecture, Performance, and Use Cases Imagine you’re reading a long book and trying to remember key plot points: The Reading AnalogyThe article delves into the comparison between Long Short-Term Memory (LSTM) and …

Author(s): Rashmi Originally published on Towards AI. Inference Is the New Training Inference Is the New Training refers to a paradigm shift where AI systems learn and adapt during inference time rather than just during pre-training. Instead of static models that only …

Author(s): Rashmi Originally published on Towards AI. Architecture Rot from Over-Reliance on AI Generation The Problem: AI generates working code but often creates architectural debt — poor separation of concerns, tight coupling, and no thought to scalability. Critical Pointers for AI Developers …