I built a prompt dependency graph that separates everything a component can reach from the smaller set that actually needs targeted evaluation.

The post Changing One Prompt Can Affect 50 Others — I Built a Prompt Dependency Graph to Find What Needs Retesting appeared first on Towards Data Science.

A lightweight runtime layer that separates instructions, evidence, memory, and tool output before they reach the model

The post AI Agents Don’t Need More Context — They Need Typed Context appeared first on Towards Data Science.

AI agents don’t just have a context problem—they have a context typing problem. When instructions, memory, retrieved evidence, and tool outputs are flattened into one string, their semantic boundaries can disappear. I built a lightweight, zero-dependency Python runtime that keeps those boundaries explicit, tracks provenance, and rejects invalid context transformations before they reach the model. This article walks through the implementation, tests, and what this approach does—and does not—guarantee.

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Adding more communication pathways between agents doesn’t necessarily improve multi-agent performance. In a controlled, reproducible experiment across 50 runs, recovery remained remarkably stable from 20% to 100% relationship density. But as the network became denser, the fraction of edges actually used fell sharply—revealing a gap between configured connectivity and behavioral connectivity.

The post Graph Engineering Isn’t About More Connections — It’s About Which Ones Get Used appeared first on Towards Data Science.