Durable Agent Memory on Azure: What Changes When Your Agent Remembers?

Microsoft's new Cosmos DB memory provider for Agent Framework moves memory from demo state to an operational design decision. Here is how to use it without making agents creepy, costly, or fragile.

AI Agent Observability: Ship Traces, Budgets, and Eval Gates Before Autonomy

Production AI agents need more than a good model. They need traces, budgets, approval events, evaluation gates, and final-state verification before autonomy expands.

OpenAI Agents API vs Azure Foundry Hosted Agents: Where Should Your Agent Run?

The new managed-agent options are useful, but the real architecture decision is where the agent loop, tools, memory, identity, and observability should live.

Durable AI Agent Workflows in .NET: From Demo Agent to Production-Ready Automation

A practical guide to designing durable AI agent workflows in .NET and Azure, with orchestration, checkpointing, human approvals, tool governance, and observability.

Agent Memory Architecture: OpenAI + Azure

A practical framework for production AI agents: separate session, task, and product memory; enforce explicit write policies; and improve reliability across OpenAI + Azure stacks.

From Semantic Kernel to Agent Framework 1.0: A Practical Migration Playbook for .NET and Python Teams

A practical migration playbook for moving Semantic Kernel/AutoGen prototypes to Microsoft Agent Framework 1.0 across .NET and Python.

Runtime Engineering Beats Prompt Engineering

Why runtime engineering—not prompt tricks—is the critical production skill for AI agents in 2026.

The New Supply-Chain Risk in 2026: What the Axios NPM Incident Teaches Every Team

Simple English breakdown of the Axios npm compromise: what Axios is, what happened step by step, and how teams should respond.

Voice AI Agents in 2026: What Works in Production (and What Still Breaks)

A practical blueprint for deploying voice AI agents in production with better reliability, safety, and ROI.

Production Agent Architecture in 2026: OpenAI Responses + MCP C# SDK + .NET

How to move from agent demos to production: contracts, observability, context budgets, and failure ladders across OpenAI, MCP, and .NET.

Agentic Systems in 2026: The Shift from Prompt Engineering to Production Engineering

Most teams are still optimizing prompts. The top teams in 2026 are optimizing architecture decisions that survive model churn. Over the last two weeks, three signals got very loud at the same time: * OpenAI pushed major Responses API capability updates (tool search, computer-use tooling, compaction patterns, and new model

Why Most AI Pilots Die in Month Two (and How to Avoid It)

Most AI pilots fail after early excitement fades. Learn the 7 operational mistakes behind month-two drop-off and the operating model that makes adoption stick.

OpenAI GPT-5.4 for Agentic Systems: What Actually Changes in Architecture

Most model update posts are feature checklists. Useful, but not enough to make architecture decisions. GPT-5.4 is one of those releases where the right question is not “is it smarter?” but “which parts of our agent stack should change now, and which should stay stable?” This guide is

I vibe-coded an AI Radio Station from my phone, and it actually worked

I’ve always had a thing for making music. Back in school, I even coded a custom DJ deck from my dorm room because the options I had felt limiting (Winamp still deserves respect). Fast forward to today, and I had one question: what if a radio station could be

Blog

MCP in .NET That Won’t Break at 2AM

Practical .NET guide to MCP C# SDK v1.0 with concrete C# patterns, retries, idempotency, observability, migration anti-patterns, and a production go-live checklist.

Build Real-Time Voice Agents on Azure OpenAI with GPT-Realtime-1.5

A practical implementation guide for production-grade voice agents on Azure OpenAI using GPT-Realtime-1.5, including architecture, latency, tooling, observability, and security.

How to Build Production Agents with OpenAI Responses API + Azure Foundry Agent Service

Most teams proved they can build an agent in a week. Far fewer proved they can run one safely in production for six months. That is the shift we are now living through in 2026: from demo intelligence to operational intelligence. The bottleneck is no longer “can the model answer?

The README for AI: How to Write an AGENTS.md That Ships

AGENTS.md is the README for AI assistants: a practical, technical guide to structure, layering, boundaries, and maintenance for better AI coding outcomes.

Azure MCP Server for Python: Build and Ship Agentic Apps with azd + Deployment Slots

A practical guide to building framework-agnostic Python agents on Azure MCP and shipping safely with azd deployment slots.

Enterprise AI Agent Architecture: A Practical Blueprint

A practical architecture blueprint for enterprise AI agents in 2026, covering orchestration, governance, RAG, observability, and rollout strategy.

AI Featured

How organizations shape their agentic systems

Agentic AI doesn’t just mirror code—it mirrors your org. By Conway’s Law, the agents you ship will look like your teams and silos. Design the agent ecosystem you want first, then align teams around it.

AI

Decoding the world of AI models

This post is part of a series that demystifies the world of AI from a software developer's perspective. In the previous blog post we covered the basics of AI and all the lingo you would hear in the hallways. In this edition we will focus on models and

A Developer's journey into AI

The digital landscape is evolving at breakneck speed, and nothing exemplifies this more than Artificial Intelligence (AI). Despite being conceptualized as far back as the 1950s, AI has truly stepped into the limelight with the advent of ground-breaking tools like ChatGPT. This technological marvel is not just a novelty,

Featured

Unleashing the power of Source Generators in .NET

In the evolving world of .NET, a new feature was introduced in .NET 5.0 that might change the way we write code - the Source Generators. They are an addition to the C# compiler that lets developers generate code during the compilation process. Let's explore how to create

The perils of over-engineering in code

In the realm of software development, finding the perfect balance between simplicity and complexity is a constant challenge. While leveraging advanced techniques and patterns can be beneficial, over-engineering can lead to code that is convoluted, difficult to maintain, and potentially inefficient. After coming across many codebases in my career