Katie Roberts - Stop Maintaining, Start Evolving: Applying AI-Native Practices to Brownfield Codebas
This talk addresses the challenge of modernizing complex, legacy 'brownfield' codebases—systems that are highly successful but burdened by accumulated technical debt and tribal knowledge. The speaker outlines how to apply AI-Native Engineering practices not for adding new features, but for architectural reclamation. Key strategies include using established patterns like the Strangler Fig Pattern and Branch by Extraction, coupled with structured processes (e.g., creating a 'plan skill') to systematically pay down technical debt while maintaining continuous function.
Key takeaways
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AI should be used for paying down technical debt, not adding to it.
17:33
Autonomous agents deployed without strict guardrails can cause havoc through over-optimization or generating 'dark code,' undermining implicit architectural constraints and creating new hidden technical debt. Safety and bounded scopes are paramount.
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Adopt a structured approach to brownfield modernization.
20:05
Instead of starting with the code, begin by conducting forensic investigations using developer input (eyewitness accounts) and creating objective data visualizations (e.g., value vs. complexity graphs) to identify high-priority areas for improvement.
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Prioritize planning over immediate migration.
25:32
In brownfield environments, the planning phase is critical. Focus on creating a structured roadmap and defining clear contracts (specs) before writing code to ensure the right thing is built.
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Use AI-assisted skills for process automation.
26:30
Implement multi-agent flows ('skills') that automate tasks like generating PRDs from documentation, creating Jira tickets, and performing detailed code mapping. This accelerates development cycles (e.g., reducing 6 months of work to 8 weeks).