AI Verification & Trust Frameworks focus on establishing verifiable provenance, runtime observability, and auditability for AI systems so organizations can demonstrate compliance, manage operational risk, and trace decisions. As of 2026, increasing regulatory expectations (e.g., operational transparency, explainability, and supply‑chain attestations) and widespread multi‑agent deployments make systematic proof‑of‑trust a practical requirement rather than an option. Key elements include cryptographic signing and verifiable credentials for model and data provenance; standardized provenance metadata and model cards; runtime telemetry and tamper‑evident audit logs; and interoperability APIs for attestation and incident forensics. Digital verification platforms and governance layers combine these capabilities with policy engines, continuous testing, and alerting. Tools in this space span infrastructure, orchestration, and specialized monitoring. Xilos positions itself as an intelligent agentic AI infrastructure and claims comprehensive visibility into connected services and agent activity—useful for runtime observability and centralized audit trails. IBM watsonx Assistant provides enterprise virtual agents and multi‑agent orchestration where embedded logging, consent handling, and compliance hooks are needed. Observe.AI brings conversation intelligence and real‑time QA for voice and chat systems, converting interactions into evidence for quality and compliance reviews. StackAI offers no‑code/low‑code agent build-and-govern capabilities, helping teams enforce guardrails and provenance controls at deployment. Together AI supplies training and inference infrastructure that supports reproducible models, versioning, and deployment attestations. Anthropic’s Claude family illustrates the need to pair deployed LLMs with provenance, safety testing, and monitoring suitable for regulated use cases. Organizations choosing verification frameworks should prioritize interoperable attestations, end‑to‑end provenance, and continuous runtime assurance to meet 2026 regulatory and operational expectations without relying on proprietary silos.