How to stop rewriting your agent’s code
Right now, you’re your agent’s context engine, it’s using your brain.
You write the rules files, maintain the skills, and paste in the Slack thread the agent missed. But the hand-curated context goes stale. When sources disagree, your agent can’t tell which is right. And none of it scales across a team. You end up getting code that compiles but doesn’t fit your system, along with a bigger token bill and longer reviews. And as teams move towards autonomous and background agents, the context gap only compounds into costly mistakes.
The teams pulling ahead use a context engine that curates context automatically: it synthesizes code, PRs, docs, and conversations, resolves conflicts, enforces permissions, and gives agents exactly what they need for the task at hand.
What we’ll cover:
- The 8-levels of AI maturity and where teams get stuck
- Why more MCPs, rules, and bigger context windows give access, not understanding
- How a context engine curates context automatically and resolves conflicts when sources disagree
- Live demo: the same coding task with hand-fed vs. automatic context
- Use cases beyond code gen: code review, bug triage, engineering Q&A, and an agent to turn Slack discussion into a context-rich PR.
You’ll walk away with:
- A mental model for moving from hand-curated context to context that stays current on its own
- Practical ways to cut token costs and review cycles
- Real examples of context-aware agents across the dev workflow
- 2 open source tools you can try today if you want to build your own context layer: Repo Rules Agent (surfaces only the rules relevant to the task) and Engineering Social Graph Builder (maps who owns which code from your PR history)
Who should attend:
- Engineering leaders driving AI adoption at scale
- Developers working with AI agents in their development workflow