February 2026

Your AI Agent Got It Right. But Did It Reason Right?

Your AI Agent Got It Right. But Did It Reason Right?

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Do You Trust Your AI Agent’s Reasoning? How knowledge graphs catch flawed reasoning Agentic AI evaluation has come a long way, and the next step is an exciting one: testing not just whether agents get the right answer, but whether they reasoned well to get there. We are getting better at testing whether agents get the right answer. We are not yet good at testing whether they reasoned well to get there. An agent can reach a correct conclusion through […]

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Architect’s Guide to Agentic Design Patterns: The Next 10 Patterns for Production AI

Architect’s Guide to Agentic Design Patterns: The Next 10 Patterns for Production AI

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21 Agentic Design Patterns Driving Enterprise AI AI Generated If you caught the first part of this series, you know we’ve already laid the groundwork by exploring several fundamental Agentic Design Patterns based on the excellent frameworks in Antonio Gulli’s book. If you missed it, you can catch up on first part here. Architect’s Guide to Agentic Design Patterns In this article, we’re picking up right where we left off to tackle the remaining patterns that turn simple bots […]

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From Notes to Knowledge: The Claude and Obsidian Second-Brain Setup

From Notes to Knowledge: The Claude and Obsidian Second-Brain Setup

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How to combine human insight with AI reasoning for next-level ideation For years, I treated my notes like a museum of unfinished thoughts. But over time, I realised I was mostly collecting, not connecting. My Obsidian vault grew into a digital labyrinth. This week, I wanted to change this and made a simple experiment. I provided Claude Desktop access to my local Obsidian Vault using the Obsidian MCP Tools to see what would happen and what usage pattern […]

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Addressing Common Misconceptions About the Model Context Protocol (MCP) from an AI Engineer’s…

Addressing Common Misconceptions About the Model Context Protocol (MCP) from an AI Engineer’s…

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Addressing Common Misconceptions About the Model Context Protocol (MCP) from an AI Engineer’s Perspective Introduction The Model Context Protocol (MCP) is a transformative standard for integrating AI assistants with external data sources and tools. However, its novelty and technical complexity have led to several misconceptions. Below, we address common questions and clarify misunderstandings, drawing from recent developments and technical resources. 1. Can MCP Only Be Connected to Agentic Systems, Not a Single LLM? Misconception: MCP is exclusively designed for […]

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Understanding the Agent Communication Protocol (ACP): A Comprehensive Guide

Understanding the Agent Communication Protocol (ACP): A Comprehensive Guide

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The Agent Communication Protocol (ACP) is a standardized, framework-agnostic protocol that enables seamless communication and collaboration among software agents, regardless of the underlying framework used to create them, such as CrewAI, LangChain, LangGraph, or others. By leveraging RESTful architecture, ACP facilitates dynamic, interoperable, and scalable agent interactions, drawing inspiration from HTTP, which transformed web connectivity in 1988. This blog provides a detailed exploration of ACP’s mechanics, features, and applications, emphasizing automatic agent selection in chaining workflows and clarifying […]

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A unified framework for evaluating the robustness of machine-learning interpretability for prospect risking

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In geophysics, hydrocarbon prospect risking involves assessing the risks associated with hydrocarbon exploration by integrating data from various sources. Machine learning-based classifiers trained on tabular data have been recently used to make faster decisions on these prospects. The lack of transparency in the decision-making processes of such models has led to the emergence of explainable AI (XAI). LIME and SHAP are two such examples of these XAI methods which try to generate explanations of a particular decision by […]

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The geometry of invariant learning: an information-theoretic analysis of data augmentation and generalization

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Data augmentation is one of the most widely used techniques to improve generalization in modern machine learning, often justified by its ability to promote invariance to label-irrelevant transformations. However, its theoretical role remains only partially understood. In this work, we propose an information-theoretic framework that systematically accounts for the effect of augmentation on generalization and invariance learning. Our approach builds upon mutual information-based bounds, which relate the generalization gap to the amount of information a learning algorithm retains […]

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Prompts Are Overrated. Here’s How I Built a Zero-Copy Fog AI Node Without Python

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Why I ditched the standard Python stack for a Kotlin-C++ hybrid to achieve microsecond-level inference on the edge. Let’s be real for a second: the AI world is currently high on “clever prompts.” Everyone is a “Prompt Engineer” until they have to build a safety system for a warehouse robot or a real-time monitor for a smart city. In the high-stakes world of Industry 4.0, a prompt is just a string. To actually do something, you need a […]

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10 Spectacular Use-Cases of Google Antigravity: Orchestrating No-Code AI Agents

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Introduction: From IDEs to Agentic Command Centers :::info We have officially crossed the event horizon. ::: For decades, the Integrated Development Environment (IDE) was a passive canvas—a glorified typewriter that waited for your keystrokes. :::warning As of February 2026, with the maturity of Google Antigravity, the IDE is dead. ::: We have entered the era of the Agentic Command Center. Antigravity is not a “coding assistant.” :::tip To treat it as such is to buy a Ferrari and push […]

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Building a ‘Second Brain’ for Marketing Using AI Agents

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Modern marketing faces a subtle but significant challenge. Not a lack of tools. Not a lack of ideas. But a lack of memory. Campaigns are launched, data is collected, and insights are discussed, but are often forgotten. Teams move on, repeat mistakes, and relearn lessons that have already been addressed. This is where the concept of a second brain becomes valuable. It serves not as a productivity gimmick, but as a fundamental improvement to marketing decision-making. With AI […]

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