I built a system that automatically discovers, verifies, and applies relevant requirements from earlier interactions without asking the user where they came from.
The post Coding Agents Don't Need Longer History — They Need Intent Continuity appeared first on Towards Data Science.
A context window can be technically complete and still describe a world that no longer exists. I built a deterministic benchmark to measure the cost of acting on stale context.
The post Context Windows Don’t Know What’s Still True — I Built a Validity Layer That Does appeared first on Towards Data Science.
How to apply the latest context engineering guidelines to your day-to-day data science work
The post Context Engineering Is Changing. Here’s What It Means for Data Scientists 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.
The post AI Agents Don’t Need More Context — They Need Typed Context appeared first on Towards Data Science.
Most coding agents treat prompt construction like retrieval: gather more files, add more context, hope the model figures it out. But that approach breaks down fast. As context grows, irrelevant code competes for attention, and when the window fills, agents start compressing their own memory—often mid-task. What looks like “forgetting” is usually just degraded context. This article explores a different approach: treating prompt construction like a compiler that decides what to keep, what to reduce, and what to discard entirely.
The post Coding Agents Don’t Need Bigger Context Windows — They Need a Context Compiler appeared first on Towards Data Science.
What it actually takes to turn a company's scattered knowledge into something an LLM can reliably use — and why the demo is 5% of the work.
The post How to Build a Context Layer and a Company Brain appeared first on Towards Data Science.
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