Field Note Nº 03·Building with AI

AI Budget: More Jazz, Less Math

September 5, 2025·2 min read

1. Context

I tried to frame AI context memory like a staffing budget: neat percentages, clean splits. My PM brain loved it. But in practice, it behaves less like math and more like music. The trick isn’t perfect allocation, it’s finding the right rhythm between backbone, values, and expertise. And while this metaphor sounds playful, the discipline behind it is real: designing agents/companions isn’t about the character count math, it’s about making intentional choices that shape outcomes.


2. What I Tried

I broke the AI context window into three roles:


3. What Happened

It sounded smart in theory, but the numbers made it feel like math when it’s really more art. My default PM brain wanted tidy percentages, but agents/companions don’t work that way. As I prototyped, I realized adding or removing KnowledgePacks shifted behavior far more than any “40/25/35” split could explain. The model responded more fluidly than a hard budget ever would.


4. Takeaway

Keep the staffing analogy, but simplify:


5. Adapt This

When you’re shaping an AI companion:


6. Reflection

AI context memory isn’t about precise allocation, it’s about integration: bringing backbone, values, and expertise together in the right proportions so the team has the most relevant companion for the project. The real skill is choosing resources so the system amplifies strategic work rather than weighing it down. I can’t predict when or if this skill will be replaced by AI. What I do know is that I’ll keep experimenting to see how these tools can support teams: showing up with less noise to fight through and more space for the fun challenges. That curiosity, even with a little anxiety, is what keeps me going.

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