Claire Gouze lays out a practical process to build and test a context layer your data agents can trust. She shows how to start small with dbt docs and markdown files, write evals, clean up data models, and iterate until you reach around 90% reliability.

• Adding query logs and profiling alone got the agent stuck around 40% accuracy; reworking the data model and documentation pushed it to 90%.
• Treat context like code: store it in files/markdown, version in Git, add CI/CD evals, and iterate on failure cases rather than plugging agents directly into raw databases.
• Start small—10 to 20 core tables with existing dbt docs—rather than trying to cover the whole warehouse at once.
• Evals should come from real BI query history or autogenerated question sets, reviewed by teams and tracked over time to build trust in the agent.

The paper examines the shift from quick “vibe coding” to a structured, agent-driven software development lifecycle. It highlights context engineering’s role in ensuring reliability and frames developers as orchestrators of AI-powered software factories. It also outlines steps to integrate AI tools without sacrificing production-grade standards.

• Vibe coding's speed trades off type checks, error handling, and dependency hygiene, making production bugs hard to trace back to their source.
• Context engineering centralizes prompts, schemas, and env vars into one source of truth so model or API swaps only require a single update.
• Developers shift from writing code to orchestrating AI components as factory managers, using tools like Airflow or Prefect to stitch containerized models together.
• The proposed four-step roadmap—catalog tools, formalize interfaces/tests, automate deployment with rollback, monitor with alerting—merges AI-driven agility with traditional SDLC rigor.

This article examines how Claude Code constructs its system prompts by analyzing recently leaked source code. It highlights the dynamic nature of these prompts, which are built from various components based on conditional statements. The author also outlines additional elements that contribute to context, like tool definitions and conversation history.

• Claude Code's system prompt isn't static text—it's dynamically assembled at runtime from conditional components, revealed by a recent source code leak.
• The prompt mixes always-included elements (like core instructions) with conditional ones (like tool-specific guidance) that only appear based on context.
• Beyond the system prompt itself, the full context sent to the model includes tool definitions, conversation history, attachments, and skills—all engineered together to shape responses.