A scripted support agent answers questions about a policy.
For the first 150 runs, the agent is configured with high effort: in about 90% of runs, it verifies the policy document with a second tool call before answering. That mirrors the mechanism in the April 2026 Claude Code case below, where a default was lowered to cut latency. Same spans, two dashboards.
Key takeaways
- One ID tying every model call, tool call, and handoff of a run together.
- Model, prompt version, effort level, harness version on every trace (Case 1).
- I didn’t benchmark any commercial observability tool.
Which tool should I pick?
Read the full article: https://www.aiobs.cloud/ai-agent-observability-vs-apm/
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