{
  "_campaign_id": "a2a-ironclaw-v0.6.2-patch2-r29-mtls",
  "_generated_by": "scripts/analyze_run.py",
  "_model": "grok-4-0709",
  "for_c_level": "Low operational risk with full pass; system is production-ready for secure federated deployments. Supports customer claims of robust, scalable AI memory infrastructure. No changes or regressions from prior runs, maintaining high confidence in release v0.6.2.",
  "for_non_technical": "The tests confirmed that AI agents can reliably share and access memories with each other in a secure setup. Every check showed memories were correctly stored, retrieved, and protected from unauthorized access. This means groups of AIs can collaborate effectively without losing or mixing up information.",
  "for_sme": "All tested scenarios (S1, S1b, S2, S4-S6, S9-S18, S20-S25, S28-S42) passed without failure modes, impacting primitives like write/read, link, consolidate, delete, and mTLS auth. No primitives affected negatively; probable root causes absent in this clean run. Testbook probes showed consistent replication across nodes with zero wrong_agent_id or visibility issues.",
  "headline": "All mTLS federation tests passed in 4-node AI memory mesh.",
  "next_run_change": "none \u2014 keep cadence with this clean mTLS baseline.",
  "verdict": "PASS \u2014 37/37 scenarios green under mTLS.",
  "what_it_proved": "Demonstrated full reliability of agent-to-agent memory sharing, secure authentication, and feature stability in a multi-node mTLS environment with no failures or skips.",
  "what_it_tested": "Exercised 37 scenarios covering memory write/read, linking, consolidation, replication, security, and admin primitives across HTTP/mTLS transport in a 4-node federated framework."
}