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 feeling it: ballooning token costs, longer review cycles, inconsistent output. And as the industry shifts toward autonomous background and cloud agents, these problems only compound.

More MCPs, more rules, and bigger context windows give agents access to information — not understanding. The teams pulling ahead have something different: a context engine that gives 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 actually is, and how it works in practice
• Real-world lessons from building a context engine at scale
• A live demo: the same coding task, with and without organizational context

You'll walk away with:

• A clear mental model for making AI agents context-aware
• Practical ways to cut token costs and review cycles
• A framework for improving output quality without overloading context
• A look at how leading teams are scaling AI effectively
• Ideas you can put to work in your own workflows this week