Using memory without poisoning future runs
Memory improves agents only when retrieval separates durable facts, accepted lessons, preferences, and temporary state.
Memory poisoning is usually a data lifecycle problem. The runner must know which context was retrieved, and the memory system must know what should be eligible next time.
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
- Separate observations from accepted facts.
- Scope memories by tenant, app, workflow, and role.
- Use confidence and freshness in retrieval policy.
- Trace which memories influenced each run.
Implementation checklist
- Mark memory source and validity window.
- Grade memory usefulness after the run.
- Retire superseded memories.
- Keep deletion and retention policy enforceable.
Watch outs
- One noisy conversation can pollute future prompts.
- Unscoped memories leak context across workflows.
- Untraced retrieval makes debugging impossible.
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.