Audience
Agent researchers, AI coding teams, workflow designers
Chris Royse field notes
Game Theory Agents Make Better Coding Decisions
A game-theory layer can frame agent decisions as incentives, tradeoffs, equilibria, and failure modes instead of one-shot suggestions.
Agents / Channel video + research directory / 5:12
Core idea
Complex coding work is strategic. Agents should reason about competing objectives, not only the next patch.
Agent Strategy
Watch on YouTube· 5:12
Game Theory Agents Make Better Coding Decisions
Frontier AI teams care about agent reliability. This is one way Chris's research connects software work to decision theory.
Watch videoOpen the full video on YouTubeWhat to take from it
The videos are raw build context. These notes translate them into the shortest useful frame for creators, companies, and AI lab readers.
Model the incentives before delegating work.
Different agents should own different decision surfaces.
Strategic reasoning is useful only when it changes implementation choices.
Continue this thread.
Related notes stay inside the same problem area first, then move to the next useful context.
Watch + read / 21:21
Context Engineering Starts With The Desired Output
Define the current state, define the target state, use memory to strengthen the path, then verify the connection.
Watch + read / 14:54
Stop Dumping Every Corpus Into One RAG Database
The swappable-brain pattern: isolated databases, provenance, cross-database search, and cleaner context for high-stakes work.
Watch + read / 14:41
Multi-Agent Search For Hard Research Questions
A multi-agent search workflow can explore large question spaces, collect evidence, and build research artifacts faster.
Make it concrete.
Send the audience, data type, target task, proof bar, and sharing limits.