{
  "_campaign_id": "a2a-ironclaw-v0.6.2-patch2-r25-off",
  "_generated_by": "scripts/analyze_run.py",
  "_model": "grok-4-0709",
  "for_c_level": "Low risk posture with one isolated failure in semantic recall, not impacting core sharing; production readiness high for basic features, viable for customer claims on federation reliability vs. prior runs. No regressions noted, but monitor semantic features before scaling.",
  "for_non_technical": "Agents mostly shared memories reliably across the network, with each agent able to see what others remembered in nearly all tests. However, in one case, a search for similar ideas didn't find a memory that should have appeared. Overall, the system works well for sharing information between AI agents, but has a small reliability issue in advanced searches.",
  "for_sme": "Failure in S18 where semantic query missed Bob's memory (marker bob-daybreak-85aa5843 not seen by Charlie); impacts semantic search primitive, probable root cause embedding or indexing flake (probe F# semantic-recall). All other scenarios clean across HTTP paths; S23 unparseable and skipped.",
  "headline": "Semantic recall failed in one scenario; most tests passed.",
  "next_run_change": "Enable debug logging for semantic embedding ops to diagnose S18 flake.",
  "verdict": "FAIL \u2014 33/34 executed scenarios passed; S18 failed, S23 skipped.",
  "what_it_proved": "Demonstrated reliable multi-agent memory propagation and recall in 33 scenarios, but revealed a failure in semantic query surfacing an expected memory in S18.",
  "what_it_tested": "Exercised 34 scenarios covering memory sharing, recall, linking, deletion, partitioning, and advanced features like consolidation, notifications, and bulk ops across HTTP transport in a 4-node federation with TLS disabled."
}