AI coding tools keep promising productivity, yet most teams aren’t seeing the payoff. The issue isn’t code generation, it’s context. Developers waste hours digging through GitHub, Slack, Jira, Confluence, and countless other systems just to understand why things were built, what decisions were made, and how the system evolved.
This talk explores context engineering: giving both humans and AI direct access to the decisions, history, and discussions already scattered across your tools. l’ll show how this contextual layer helps AI models like Claude and Cursor actually understand your production system, instead of guessing.
The result is less hallucination, faster iteration, fewer interruptions, and AI that’s finally useful in production codebases. If you care about making AI work for real development teams, this is the foundation you can’t ignore.
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