I wrote recently about Azure Agent Mesh and OpenSharing — two infrastructure layers that between them cover how enterprises register, discover, share, and execute agents. Between them, they address a lot of the plumbing that has been missing from the enterprise agent stack. But there's a gap neither of
Unity AI Gateway, announced at DAIS this week, is the feature I've been waiting for since Agent Bricks shipped last year. It's a centralized governance layer for model access in Databricks — you configure which models are approved for use in your environment, who can call them,
The performance gap between open-weight models and closed frontier models has spent the last year collapsing faster than anyone predicted. Epoch AI's tracking puts open weights at roughly a three-to-four-month lag behind state-of-the-art closed models on average. For coding tasks, the gap has effectively closed — DeepSeek V3.2
The central message from DAIS this week, delivered by Ali Ghodsi in the opening keynote, was direct: AI doesn't have an intelligence problem, it has a context problem. If your CFO can't get an AI system to explain why margins changed, that's not a
Two and a half years in, with a production knowledge system, a working orchestration layer, local inference running alongside cloud providers, and prompt hygiene enforced at the infrastructure level — I want to be direct about something the AI industry is not particularly incentivized to say clearly: building this has been
Anthropic announced Claude Fable 5 and Claude Mythos 5 yesterday — the first models in the new Mythos-class tier that sits above Opus in the model family hierarchy. Fable 5 is the accessible tier of the Mythos class; Mythos 5 is the top of the stack. If you've been
Claude Opus 4.8 shipped yesterday, emphasizing honesty and reliability as the headline characteristics alongside the Dynamic Workflows research preview in Claude Code. The reliability framing is an interesting choice — it's a signal that Anthropic sees predictable, trustworthy behavior in agentic contexts as the frontier to push on,
Azure Agent Mesh, announced at Build this week, is Microsoft's answer to a problem that's been building for the past year: as organizations deploy more agents — in Copilot Studio, in Azure AI Foundry, in custom LangGraph deployments — the coordination problem grows. How does an agent in
Prompt hygiene is not a glamorous topic. It doesn't make a good conference talk. It doesn't generate engagement on social media. It is, however, the difference between an AI-assisted workflow that you can trust with real work and one that will eventually cause you a problem
The headline from Microsoft Build this week that landed hardest in the developer community: Project Polaris, Microsoft's own foundation model, replaces GPT-4 in GitHub Copilot by August. After years of deep dependency on OpenAI, Microsoft is shipping its own model into its flagship developer product. That's
The open-weight vs. closed-API model decision has gotten more nuanced and more contentious over the past 18 months. There are strong opinions on both sides that are often driven by ideology rather than analysis. Here's the framework I actually use with clients, which is grounded in what matters
Meta's Llama 4 Behemoth — 2 trillion total parameters, 288 billion active per token — is the largest open-weight model ever previewed. The numbers are designed to impress, and the benchmark results on STEM reasoning and mathematics are genuinely strong. But "biggest model wins" is a lazy frame,