To the moon: navigating legacy code better

When developers face legacy code, it's often like space explorers confronting an alien landscape. In a recent panel discussion, Forrest Brazeal and Matt Ball from Augment explored how AI agents are transforming the way engineers understand and update complex codebases. Their conversation reveals a fundamental shift in how we might approach the age-old problem of deciphering and improving code we didn't write.

Key insights from their discussion:

• Context is king – Understanding legacy code requires more than just reading the source; it demands grasping the broader system context, decisions that shaped it, and business logic embedded within it.

• AI agents can serve as "code archaeologists" – Rather than just providing snippets, sophisticated AI tools can maintain context across multiple files and conversations, offering a more comprehensive understanding of complex systems.

• The "mechanical sympathy" problem – Developers need to understand not just what code does, but why it exists and how it interacts with other systems—something traditional documentation often fails to capture.

Why this matters more than you might think

The most compelling insight from this discussion is how AI agents can function as institutional memory for software organizations. As Ball pointed out, these tools can analyze commit histories, documentation, and code patterns to reconstruct the "why" behind technical decisions—addressing what's often called "the tragedy of lost context."

This capability represents a potential sea change for organizations struggling with knowledge transfer. When senior engineers leave or projects change hands, critical context about design decisions often disappears. According to a 2022 McKinsey study, large enterprises lose an estimated 20-30% of productivity during knowledge transitions. AI agents that can reconstruct this missing context could dramatically reduce this efficiency loss.

What they didn't mention: the human element remains critical

While Brazeal and Ball rightly emphasize the power of AI for code comprehension, there's an important complementary perspective: humans still need to ask the right questions. The most effective implementations of code-understanding AI tools I've seen combine AI capabilities with human expertise in a specific way.

Take Shopify's experience implementing code navigation tools. Their engineering team found that AI tools were remarkably good at explaining what code did, but engineers still needed to frame the right initial questions. The most successful teams developed a practice