Writing
From the lab
Writing on building, running, and owning AI agents in production.
Quality Intelligence: engineering trust into AI features
Shipping AI features that hold up in production takes more than model testing. It takes a Quality Intelligence framework built on reliability, clarity, and measurable excellence.
Why RL environments beat prompt engineering for edge cases
Prompts are instructions. Environments are practice. Here's why the distinction matters when your agent keeps failing on the same class of inputs.
The hidden cost of frontier models in enterprise workflows
Most teams don't realize how much of their API bill comes from a small set of repetitive tasks. We traced the pattern across 12 deployments.
Trace capture without slowing down your agent
Observability shouldn't be an afterthought. Here's our async capture architecture that adds less than 5ms overhead to any LLM call.
What we learned building private SLMs for three different verticals
Finance, legal, and ops all have different failure modes. Here's what we found when we trained domain-specific models for each.
