AI Con USA 2026 - AI in the SDLC
Thursday, June 11
From Prompt to Production: Building AI Systems That Actually Work
AI can now generate code, tests, documentation, and even entire applications in minutes. Yet many organizations are discovering that faster code generation has not translated into faster delivery, higher quality, or greater business impact. Instead, teams are often facing more code churn, more technical debt, and more production incidents. The challenge is no longer getting AI to write code. The challenge is building systems that consistently deliver value to end users. Drawing on real-world experience building AI products, agent-based systems, evaluation frameworks, and production...
User-centricity for AI-assisted Test Engineers
Traditional software testing is fundamentally deterministic: the same inputs must always produce the same outputs. Yet many teams introduce AI into their testing without first defining the problem the AI is meant to solve, leading to brute-force experimentation and unreliable results. Google’s 2025 DORA report highlights that user-centricity is a prerequisite for AI success and that AI is most effective when it is pointed at a clear problem. AP shows you how that insight applies technically to testing. Before AI can be used as a testing tool, it must first be tested and understood in the...