// Posts

Putting the pieces together: how I’m using agentic systems in my daily workflow and the patterns that have stuck along the way.

I started by asking whether autoregressive LLMs could ever reach AGI. The better questions turned out to be what AGI means and where we draw the boundary around the system expected to achieve it.

Runtime QA is judgment from end to end, so let the agent own the investigative loop. Separate its judgment from reviewable mechanics, stop for the decisions that carry consequences, and leave the last word with a person.

The most important question in AI architecture isn’t which model you use. It’s who owns the control loop. That choice affects cost, reliability, and how easily you can swap providers. In this article, I look at three categories of workflows and explore why the nature of the problem should determine the harness, and why code, rather than the model, should often drive the loop.

LLMs are stochastic by design. Reliable agent workflows come from surrounding that chaos with deterministic code, not from wishing the model were predictable.

A self-described tech curmudgeon on how generative AI rekindled the urge to tinker, explore, and build outside the comfortable set of tools built up over the years.

Two Salesforce engineers describe separating a Django API framework from a legacy application and navigating the process of releasing it as open source.

Originally published in Stack Overflow Blog.