A context window is a budget
Every coding-agent request competes for a finite context budget. Filling it with an entire repository, stale transcripts, or unrelated logs can make the model less precise while leaving less room for the current task and tool results.
Begin with the task, the active file or error, relevant Git diff, and a focused search. Let read-only tools gather more evidence only when the initial set cannot support a decision.
Keep durable facts separate from raw history
A workspace summary, implementation plan, selected files, and verified constraints can remain useful over time. Raw conversation and tool output become stale quickly. Store the former as bounded session or workspace memory; refresh the latter from the live repository.
This is especially important after an interruption. The agent should not assume a file still matches yesterday’s tool output simply because it appears in a transcript.
Measure context quality by reviewability
Good context lets a developer explain why the agent touched a file and why the proposed test is relevant. If an agent cannot connect its answer to selected evidence, add precision to the task before adding more tokens.
Truss presents context and workspace activity beside the agent workflow so developers can start small, inspect what was used, and expand only when necessary.
Next steps
Put it to work in your own workspace.
Pick the surface that fits your day, then connect a model you control.
FAQ
Questions developers ask before switching.
Should I give a coding agent my whole repository?
Usually no. Start with targeted files, a diff, an error, or a directory, then let the agent inspect additional evidence through read-only tools.
Does a larger context window solve bad agent results?
Not by itself. Selection, freshness, tool reliability, and a clear task often matter more than raw capacity.