Kevin Welty

Leveraged agentic AI to handle the creation of backend systems for collecting and decomposing data for analytics display — eliminating the need to hand-code each solution. By delegating implementation to the agent, I can drive multiple workstreams simultaneously rather than context-switching between them one at a time. The result is a dramatic increase in throughput: issues that would normally queue up get worked in parallel, and my focus shifts from writing boilerplate to directing the agent and validating outcomes.

Built a system of custom skills and structured prompts that teach AI models to internalize and conform to company-specific coding standards. Rather than relying on engineers to manually review AI-generated code for style and convention violations, the AI is trained to understand naming conventions, architectural patterns, file structure requirements, and language-specific rules enforced at the organization. The result is AI-assisted development that produces code consistent with the existing codebase from the first output — shortening review cycles, reducing back-and-forth, and making AI a reliable contributor rather than a source of technical debt.

Integrated Claude directly into production systems to serve as an intelligent monitoring layer. Rather than relying solely on static threshold-based alerting, Claude continuously analyzes system output, logs, and data patterns to identify errors and abnormalities that rigid rules would miss. When something unexpected is detected — whether an unusual data shape, an unexpected failure mode, or behavior outside normal operating parameters — the system automatically fires an alert with a human-readable explanation of what went wrong and why it's notable. This brings LLM-level reasoning to observability, catching subtle issues earlier and giving engineers context rather than just raw noise.