The biggest challenge facing an agent harness is context engineering, or what information an agent gets, and when.
The longer the task, the harder it is to keep the context window from overloading.
Deep Agents ships with this built in: filesystems, subagents, and skills, already wired up for context management.
context engineering — Deciding what an agent sees at each step — the discipline that replaced prompt engineering once context windows got large enough to fill badly.
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
Why it matters
Bundling filesystems, subagents, and skills as default context windowThe maximum amount of text a model can consider at once — its working memory for the current conversation or task.Full definition →-management primitives gives AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → builders a concrete pattern for keeping long-running tasks from blowing the context window, rather than reinventing it per project.
