How to stop babysitting your agents

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. And as the industry shifts toward autonomous background and cloud agents, these problems only compound. 

More MCPs, rules, and bigger context windows give agents access to information, but not understanding. The teams pulling ahead have a context engine to give agents exactly what they need for the task at hand.

What we’ll cover:

• Where teams get stuck on the AI maturity curve and why progress stalls
• Why naive RAG, bigger context windows, and more rules don’t solve the root problem
• What a context engine is and how it actually works in practice
• Real-world lessons from building a context engine at scale
• A live demo comparing agent output on the same coding task, with and without organizational context

You’ll walk away with:

• A clear mental model for how to make AI agents context-aware
• Practical insight into reducing token costs and review cycles
• A framework for improving output quality without overloading context
• An understanding of how leading teams are scaling AI effectively
• Concrete ideas you can apply immediately to your own workflows
• 2 open source tools you can get try out today

Who should attend:

• Engineering leaders driving AI adoption at scale 
• Developers working with AI agents in development workflow