Your agents are fast, capable, and completely context-blind. They generate code that compiles but doesn't reflect how your system actually works. You're likely already seeing the impact: ballooning token costs, longer review cycles, and inconsistent outputs. More MCPs, rules, and bigger context windows give agents access to information, but not understanding. In this session, we dissect how teams pulling ahead use a context engine to give agents exactly what they need for the task at hand. Includes a short demo showing a real coding task with and without a context engine.
Summary
Speakers

Brandon Waselnuk
Developer Relations @ Unblocked
Brandon Waselnuk is a Developer Advocate at Unblocked, the context layer for agentic software development. He previously led product at Mintlify and co-founded Squire AI, an AI code review platform. A Y Combinator alum (S21) and Venture Partner at Pioneer Fund, Brandon also led product initiatives at IBM, helping create Design Thinking @ IBM and managing portfolio planning for IBM's $1B+ Business Analytics division. He's passionate about building tools that make engineering teams more productive.
