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Latest — 02 Jun 2025

Streamline Agentic AI Engineering with MCP OpenAPI Proxy and MCPO

MCP OpenAPI Proxy is a Python-based MCP server that dynamically converts APIs, defined by OpenAPI specs, into MCP-compatible tools that AI agents can invoke. MCPO complements this by exposing MCP tools as RESTful HTTP APIs, instantly making them accessible to services outside of the MCP ecosystem.

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Data Engineering is a Cost-Saving Catalyst in Agentic AI

Data engineering powers Agentic AI by delivering clean, structured, and purpose-ready data. Efficient pipelines and pre-aggregation reduce compute costs, speed up decision-making, and scale autonomy—turning backend optimization into a key driver of intelligent, cost-effective systems.

Agentic AI Matched with MindsDB is a Powerhouse

The next wave of artificial intelligence is agentic – AI systems capable of proactively gathering information, reasoning, and acting to achieve goals. Unlike a simple question-answering bot, an agentic AI can plan and execute tasks autonomously, often by interacting with various data sources and tools. Building such systems is challenging because

Anthropic’s Model Context Protocol (MCP) for Tool-Enabled Agentic AI

MCP, or Model Context Protocol, is emerging as a prominent standard for engineering agentic AI tools and functionalities. Understanding how to leverage the capabilities of this standard to develop AI-centric products will equip engineering teams for rapid and efficient product development.

MCP vs OpenAI’s OpenAPI Tools: A High-Level Comparison

Overview Model Context Protocol (MCP): MCP is an open standard introduced by Anthropic in late 2024 for connecting AI models with external tools and data sources . It aims to replace the many fragmented, one-off integrations in AI applications with a single unified protocol . In Anthropic’s words, MCP provides a

Writing Effective Instructions for Marvin 3.0 AI Agents

Marvin 3.0 is a Python framework for building agentic AI workflows, where tasks are delegated to Large Language Model (LLM) agents . A core idea in Marvin is that each unit of work (a Task) should have a clear objective described by instructions, and one or more Agents specialized to

Comparing MCP, Marvin 3.0, and LangChain for Context-Aware LLM Orchestration

Introduction Large Language Models (LLMs) have opened new frontiers in AI applications, but leveraging them effectively requires more than just prompting. Developers need ways to orchestrate context-aware reasoning and memory – feeding the right data to the model at the right time, and enabling multi-step decision making with tools or knowledge

Exploring EEG Data and Designing a PostgreSQL Schema for Machine Learning

Electroencephalography (EEG) is a fascinating window into the brain’s activity. By placing electrodes on the scalp, researchers and clinicians can capture the electrical signals produced by neurons firing in sync. These brainwave recordings have applications ranging from medical diagnosis to cutting-edge brain-computer interfaces. In this article, we’ll introduce

Artificial General Intelligence vs. Artificial Intelligence

The key difference between Artificial General Intelligence (AGI) and Artificial Intelligence (AI) lies in the scope and flexibility of their capabilities:

Agentic AI Engineering: Definition and Differences

Agentic AI Engineering refers to the design and development of AI systems capable of autonomously making decisions, initiating actions, and achieving goals in dynamic, real-world environments. These systems exhibit agency, meaning they operate beyond simply responding to user prompts or predefined tasks. They leverage long-term memory, reasoning, and goal-setting capabilities