I set out to build a memory layer for my agents — somewhere they could write what they learned and read it back later without me hand-feeding context into every prompt. A few weeks in, I looked at the schema I'd drawn and laughed, because I'd reinvented
Most agent-authorization schemes I've seen ask the wrong question. They gate on confidence: is the model sure enough to act? Set a threshold, and if the score clears it, the agent does the thing. It feels rigorous. It's backwards. The axis that should decide whether an
I ran git shortlog -sne on my orchestrator repo last week, the way you do before writing a release note and wanting to thank the right people. I scrolled to the top expecting my own name. It wasn't there. The top committer is forgeai-dev. Second place goes to
I keep two completely different reflexes in my head for the same task, and last week they finally collided. The task is ordinary: an app needs a real database — proper transactions, low-latency reads and writes, not an analytics table playing dress-up. For years my hand went to the same place
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