RSCH
Research
Long-form investigations into how AI agents behave in real engineering work. Each programme is evidence-labelled, sequenced as a learning path, and ends with something you can do on Monday.
What every research programme gives you
- A one-page summary to decide in five minutes whether it matters to you.
- A learning path in parts, from the problem to a plan you can run on Monday.
- Evidence labels on every number, and a sources page that says what was not verified.
- Diagrams, charts, templates and code you can copy.
- An FAQ and a glossary for the questions and terms that come up most.
Programmes
RSCH-001PublishedContext Management in Coding AgentsLarger context windows did not make coding agents proportionally more reliable. The engineering problem is deciding what the agent should see, when it should see it, and what that context costs.Open the research →
In preparationLoop EngineeringHow is an agent's iterate-and-repair loop built, and what makes it converge instead of spin?
In preparationAgent EvaluationHow do you know an agent actually got better, with what design, at what cost?
In preparationPrompt Cache ArchitectureWhat does editing a file mid-session really cost once the prompt cache is priced in?
In preparationMulti-Agent Maturity ModelWhen is a team actually ready for multi-agent systems, and what has to be true first?