Every product team building with AI faces the same question: where does the AI interaction go? Not the model or the prompt, but the affordance: the surface the user sees and interacts with. For my own reference and discussions I wanted to capture these. Even though the chat box is where this all got started, these are not stages of
When I read about the Software Factory concept from StrongDM AI, I was intrigued. In part because of the ability to produce an impressively large and complex component. But even more so with how little it might take: could you really do that simply by taking pre-existing specs from the internet and have agents search the program space for
Coding agents run a search in program space. That's the closest analogy I can find for how to use agents productively. If you think of it as an assistant to whom you give tasks, you'll generally be in too tight a loop, giving feedback every few minutes. But the search analogy forces you to consider: 1.
There are only three categories of guardrails to prevent harm from agents. First, relying on hard constraints to only allow certain kinds of behaviour. For example, limiting which tokens can be decoded (structured output) or exposing only a specific set of tools to an agent. Assuming correct implementation, these guarantee certain behaviours won't be possible. Second, the LLM&
I committed to sharing something every day in March about our AI transformation at Pactum. The pace is high, not just internally but in the entire world, so there's a lot that happens and a lot to share. This is the week 1 recap, pulling together what I've learned across the posts, the meetings, and the
As our employees use Claude Code for various tasks, we keep seeing agents repeat the same work. Summarizing a customer's state across Salesforce, email, product analytics, usage data: every agent pulls together the same picture from scratch, every time. Martin Kosk, our enterprise architect, proposed a useful frame for thinking about this: core vs. edge. Edge is the
"Nobody gets fired for buying IBM" used to mean: pick the vendor nobody will question. It was a decision driven by fear, not conviction. I think Anthropic is becoming the equivalent for AI, but for a better reason. At Pactum, we didn't choose Anthropic through structured evaluation. We started with Gemini because we're on
I've seen people try Claude or ChatGPT, get mediocre answers, and conclude AI is overhyped. But the power of agents doing real work for every employee comes from the agent knowing your company, your team, your processes. If you use the vanilla version and expect it to be mind-blowing, you're testing the wrong thing. Exceptional
In part 1 I covered the straightforward agent use cases: copyediting, onboarding, expense reports, Jira management. Those are about doing existing tasks faster. The five below are different. They share a thread: the agent isn't replacing someone's work, it's gathering context I wouldn't have gathered on my own, so I can think
While we're in this mode of rapidly developing agent capabilities, I want to do my part in diffusing knowledge of what agents can do. Since Claude Code IS the software, I am using it more and more for everyday office work, and rapidly discovering new things that agents can (or cannot) do. My daily driver with those is
