🔥 What if managing AI agents was as simple as deploying Kubernetes?
We're launching the Alpha version and inviting early adopters to shape the next evolution in context-driven AI infrastructure.
MCP Intent-Based Server: Simplifying Context for Complex AI Integrations
I have collected a list of the most popular MCP servers hosting services available today. This guide will help you navigate the competitive landscape of Model Context Protocol (MCP) servers, focusing on their unique features, strengths, and weaknesses. Yes, is confusing, "servers" and "hosting", "tools" and "frameworks", "agents" and "servers". But don't worry, I will explain everything in detail.
This is a rapidly evolving field, and new players are emerging all the time. Whether you're a developer, project manager, or executive, understanding the MCP server landscape is crucial for making informed decisions about your AI projects.
This guide will help you navigate the competitive landscape of Model Context Protocol (MCP) servers, focusing on their unique features, strengths, and weaknesses. This is a rapidly evolving field, and new players are emerging all the time. Whether you're a developer, project manager, or executive, understanding the MCP server landscape is crucial for making informed decisions about your AI projects.
If you have an OpenAPI Specification (OAS) or gRPC IDL, you're closer to running an MCP Server than you think. You've already done 10% of the work by defining your API. But what about the rest?
Agentico's workflow for transforming API specs into a fully functional MCP server.
AI agents are becoming essential for automation, decision-making, and intelligent task execution. While some envision a future where AI autonomously builds its own agents, the current reality is different. Today, building effective AI agents is still a software engineering challenge - one that requires careful design, the right frameworks, and seamless system integration.
The misconception? Many believe that AI agents require machine learning expertise. But in reality, building AI agents is more like developing microservices - it's about structuring software components, defining APIs, and ensuring smooth communication between systems.
Workflows power every business process, from approving vacation requests to orchestrating complex supply chains. As enterprises grow, their workflows must evolve. We've seen a big shift from simple rule-based systems to AI-driven solutions. Now, a new layer is emerging: "agentic" workflows, where AI not only gives insights but also acts on them.
Let's explore three key stages:
The GoF (Gang of Four) design patterns were game changers in software engineering arena, same as the "Microservices pattern language" by Chris Richardson redefined distributed systems. Now, a new set of patterns is emerging in the world of Generative AI (GenAI), we are witnessing a similar revolution, one that is transforming AI from a proof-of-concept novelty into a reliable, production-ready tool. These patterns are not just interesting, but essential for building robust, scalable, and reliable AI products.
How can these patterns help you build smarter, more robust AI products? Let's dive in.
The AI landscape is evolving at breakneck speed, and one of the most exciting developments is the Model Context Protocol (MCP). MCP is a powerful tool that enables Large Language Models (LLMs) to interact with external data and tools seamlessly. However, to unlock its full potential, we need to address a critical challenge: standardizing MCP implementation.
In this post, we'll explore why MCP needs a standardized implementation pattern and how Agentico can help revolutionize AI development.
Agentic AI systems thrive in distributed environments, where multiple components interact dynamically. These systems orchestrate AI models, APIs, and data pipelines across diverse infrastructures - cloud, edge, and airgap environments.
But with this complexity comes a challenge: How do you monitor and troubleshoot AI-driven workflows in real time?
GitHub has entered the AI model hosting space with GitHub Models, a platform that allows developers to find, experiment with, and integrate AI models directly into their workflows. The latest addition to this service is DeepSeek-R1, a powerful large language model (LLM) now available in public preview.