Evidence trails for autonomous decisions
5 Jul 2026 · 6 min read
Every important agent decision should preserve inputs, retrieved context, policy checks, and output evidence.
An evidence trail lets humans review autonomous decisions after the fact and improve the system before the next run.
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
- Capture the question the agent answered.
- Store retrieved memories and tool outputs.
- Record policy checks and approvals.
- Attach final artifact and validation result.
Implementation checklist
- Link evidence to trace spans.
- Redact sensitive data.
- Keep evidence retention by action risk.
- Make evidence exportable.
Watch outs
- Evidence collected after failure is often incomplete.
- Too much raw evidence can hide the important signal.
- Access to evidence must be governed.
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.