Llama 4, DeepSeek V3.2, Mistral Large 3 — the open-weight model landscape at the end of 2025 is genuinely competitive with closed frontier models for a wide range of tasks. That's the good news. The deployment reality has some friction that's worth understanding before you
The economics of AI model access shifted faster in early 2026 than anyone I spoke to had anticipated. Price per token changes, tier restructuring, capability gating behind higher subscription tiers, and model deprecations on compressed timelines — these arrived in close succession and in ways that exposed an uncomfortable dependency for
It's been a year of a lot of announcements. Stepping back from the release cadence, here's what I've seen actually prove out in production environments — not in demos, not in conference keynotes, but in the client work and my own infrastructure. 1. The Agent-
Two years changes the question. In the first year, the question was "does this work?" The answer was yes, with significant caveats. In the second year, the question became "is this sustainable?" — and the answer to that required being honest about what the system had cost
There's a framing I keep coming back to when talking to clients about their AI strategy: the question isn't "should we build an AI platform" — the question is "do we have the data infrastructure to support AI that actually works?" The models
Provider agnosticism is one of those design principles that sounds like obvious good practice until you're in the middle of a feature and the easiest path is to use a provider-specific API. That's when the principle either holds or it doesn't. I'
Microsoft Ignite landed this week and the message was cleaner than I expected: agents are the new application layer, Fabric is the data substrate, and Copilot is the interface for everyone from developer to executive. The "Frontier Firm" framing they're using positions an AI-first organization
A year ago, most production pipelines that used LLMs had a single model choice baked in. Today, with the open-weight landscape as competitive as it is and multiple model families offering genuinely different capability-cost tradeoffs, building a routing layer is worth the effort. Here's the framework
Running a system in production against real work is different from running it against test cases you designed to make it succeed. The production gap — the difference between what you built and what you needed — shows up in ways that are difficult to anticipate and impossible to fully test for.
With Llama 4 Scout's 10-million-token context and Claude Sonnet 4.5's 1-million-token beta window, a reasonable question is whether RAG is still necessary. If you can fit your entire knowledge base in a context window, why build the chunking, embedding, and retrieval
Adopting a framework means committing to its mental model. The longer you use it, the more that mental model shapes how you think about the problem space. LangGraph's mental model — workflows as directed graphs with typed state flowing through nodes — turned out to be a good fit for
There's a question anyone faces before building custom tooling: why not use what already exists? It's a reasonable question, and the answer shouldn't be "because I prefer to build things." It should be a specific list of requirements that existing solutions don&