# Your traces know what happened. Your data knows if it mattered.

Attach internal context such as user, account tier, version or any custom metadata to every trace with plain span attributes. Then slice quality, cost and evals by the dimensions your business runs on.

## The problem: A trace without context is just timing

Knowing a run took 4.2 seconds is trivia. Knowing it was a premium customer's checkout, on last week's release, in the EU region? That's the difference between a dashboard and a decision.

## How RunAgain solves it

1. **Metadata that becomes queryable.** Session, conversation, user, agent, environment, version, release, tags and a free-form metadata bag are promoted to typed columns: fast to filter, chart and join, not buried in JSON blobs.
2. **No mapping to maintain.** RunAgain accepts the OTel GenAI keys plus common vendor aliases, so traces from most SDKs enrich correctly with zero configuration. Resource attributes apply to every span from a process; span attributes win when both are set.
3. **Evals and alerts scoped by your data.** Run the expensive LLM judge only where metadata.tier = premium; run strict structured-output checks only on the checkout agent. Enrichment isn't decoration; it routes your quality budget.
4. **Probes: capture the calls you don't own.** The sandbox proxy captures your agent's outbound HTTP calls, third-party APIs and internal services included, and lands them inside the same trace via trace-context propagation. The tool side of the loop stops being a blind spot.

## Related solutions

- [Agent observability](https://runagain.ai/solutions/agent-observability.md)
- [Goal optimization](https://runagain.ai/solutions/agent-goal-optimization.md)
- [Monitoring & alerting](https://runagain.ai/solutions/agent-monitoring-alerting.md)

Book a demo: https://cal.com/tamas-szuromi/30min · Get in touch: tamas@runagain.ai

---

Markdown mirror of https://runagain.ai/solutions/enrich-agent-traces for agents and LLMs. Append .md to any runagain.ai page URL for its markdown twin. Overview: https://runagain.ai/llms.txt · For agents: https://runagain.ai/for-agents.md