I recently put together a talk called "Agents, Tools, and MCP, oh my!" that tries to cut through some of that noise. As developers, we are being handed a firehose of new tools and technologies, and I wanted to spend my session doing something a little different: break the pieces apart, reduce some of the complexity and overwhelm, and then build them back up so they actually fit together.

GraphRAG takes this a step further. Instead of retrieving only text chunks, it can use graph relationships to retrieve connected context, following relationships between entities to build richer, more structured context. The result can provide answers grounded in both data and the relationships between that data.

Most Glue pipelines start simple and become difficult to manage gradually — formulas get hardcoded, modules grow without boundaries, output files proliferate, and before long a single job is doing too many things in ways that are hard to test, hard to debug, and expensive to change.

Looking back, the bash script wasn't really solving debugging. It was solving prioritization. I just didn't have the vocabulary for it yet.

If you're building a data platform on Azure in 2026, you're going to be asked this question: Azure Databricks or Microsoft Fabric? Both run on Delta Lake, both integrate with ADLS Gen2, both have Spark, and both promise to be your unified data platform. The overlap is real, and the marketing doesn't help.

This post is an honest breakdown of where each genuinely excels, where they overlap, and how to decide without getting lost in feature comparison tables.

The agent loops on itself and burns through your token budget. After shipping a few of these systems, I have learned that the durable design question is not "can the agent call a tool" but "what happens when the tool call goes wrong."

In this tutorial, I'll walk through building a production-grade feature engineering pipeline on Azure Databricks using:

In this post, I'll walk through building an agent system that reacts to streaming user behavior in real time using:

These features come from PR #5133 (WebSockets) and PR #5141 plus PR #5099 (the typed clients).

With that one key, the attacker walked straight into the U.S. Department of the Treasury, reset internal passwords, accessed workstations inside the Office of Foreign Assets Control, and read unclassified documents before anyone noticed. The Treasury disclosed it to Congress on December 30. The Department of Justice indicted the alleged operators in March 2025.

Not because the AI model couldn't identify Docker container failure scenarios—it usually could. The failures occurred at the decision boundary: determining when an automated action was appropriate, when escalation was required, and when no action should be taken.

• One has already been tested in lower environments, but is still awaiting business approval
• The other is fully validated and ready to go live

Naturally, you want to release the second feature to production.

That is a useful thing. It is not a platform.

One day, someone confidently said, “An MCP server is basically a tool.” Another person immediately disagreed and replied, “No, no — MCP is more like a client.” Before that debate could settle, someone else joined the conversation and said, “Actually, MCP is just a protocol.”

Code Patterns in Spring Boot With Kafka

Spring for Apache Kafka gives me two primitives that feel pleasantly old Spring KafkaTemplate for sending and @KafkaListener for receiving. That template/listener model is intentionally similar to other Spring integration tech, which keeps application code focused on domain logic instead of raw client plumbing. 

This was not a standalone exploit. It was a multi-vector convergence where vulnerabilities in the Node/NPM ecosystem, the systemic ungoverned architecture of the Visual Studio Code Marketplace, and the tactical “fog of war” caused by a period of historic GitHub infrastructure instability came together to create the perfect attack.

Azure AI Foundry has a genuinely great portal. You can see your agent runs, the tools it calls, the messages it sends and receives, and even a breakdown of token usage — all in a clean UI.

But here's what actually happens when you're building an agent locally:

This article walks through registering the server, the full tool catalog it advertises, how Foxit’s eSign and DocGen REST APIs carry the same agent session forward into signing and document generation, and a concrete four-step workflow you can reproduce with your own files.

The API key was never in its environment.

Quick Intro

• Sign up at https://x.ai/api.
• Generate an API key from the console.
• Install pip install xai-sdk.
• Set env var: export XAI_API_KEY="your_key_here".
• Models list: https://docs.x.ai/developers/models.

I'll share some samples in Python.