Context caching vs retrieval for lower spend
Lower agent spend by caching stable context and retrieving durable facts instead of replaying whole histories.
Context cost grows when every run rebuilds the same prompt. Caching and memory retrieval attack different parts of that waste.
AstraRunner treats this as an execution-platform concern: the run needs durable state, traceable decisions, cost visibility, and enough structure for teams to review what happened after the agent finishes.
Patterns that work
- Cache static policy, schemas, and runbook text.
- Retrieve durable project facts and accepted lessons.
- Summarize long artifacts before reuse.
- Measure output quality after context reduction.
Implementation checklist
- Separate stable context from task-specific context.
- Invalidate caches on policy changes.
- Trace which memories were retrieved.
- Compare token spend before and after retrieval changes.
Watch outs
- Stale cached policy is a governance risk.
- Retrieval can add noise if not scoped.
- Over-compression can remove acceptance criteria.
Related reading
How this fits the Astra stack
AstraRunner owns the operational path for agent work: orchestration, scheduling, approval gates, traces, costs, and role-agent handoffs. AstraMemory owns durable context and retrieval. AstraGenie turns those capabilities into product workflows for teams that want automation without rebuilding the platform layer.