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Agentic AI is moving fast. In post one of this series, we looked at why agentic AI will fail without an AI gateway — the risks of cost sprawl, brittle workflows, and runaway complexity when there’s no unifying layer in place. In post two, we showed you how to tell whether a platform qualifies as […]

Agents are here. And they are challenging many of the assumptions software teams have relied on for decades, including the very idea of what a “product” is. There is a scene in Interstellar where the characters are on a remote, water-covered planet. In the distance, what looks like a mountain range turns out to be

If you’ve ever burned hours wrangling PDFs, screenshots, or Word files into something an agent can use, you know how brittle OCR and one-off scripts can be. They break on layout changes, lose tables, and slow launches. This isn’t just an occasional nuisance. Analysts estimate that ~80% of enterprise data is unstructured. And as retrieval-augmented

OpenAI hasn’t released an open-weight language model since GPT-2 back in 2019. Six years later, they surprised everyone with two: gpt-oss-120b and the smaller gpt-oss-20b. Naturally, we wanted to know — how do they actually perform? To find out, we ran both models through our open-source workflow optimization framework, syftr. It evaluates models across different