Proof‑of‑Trust and digital verification frameworks address how organizations establish, attest and audit the identity, provenance and behavior of AI components and agents. This topic covers techniques and platforms that combine policy, observability, model provenance, vendor governance and documentation to create verifiable chains of trust for models, multi‑agent workflows and third‑party services. It’s timely in early 2026 because enterprises face rising regulatory scrutiny, larger multi‑agent deployments, and greater emphasis on model supply‑chain transparency and automated compliance. Key tool categories include AI governance platforms (policy centralization, monitoring, validation), agent orchestration and observability systems, model and production platforms that surface provenance and privacy controls, and knowledge/workflow systems that record evidence and audit trails. Examples: Monitaur focuses on insurance and regulated industries by centralizing policies, monitoring and vendor governance for compliance; Kore.ai and StackAI provide no‑code/low‑code platforms to build, deploy and govern multi‑agent workflows with observability and controls that support attestation and runtime verification; Mistral AI offers enterprise models and a production stack that emphasize model provenance, efficiency and privacy controls; Notion serves as a workspace for documenting verification artifacts, runbooks and audit records. A practical Proof‑of‑Trust strategy combines: standardized identity/attestation for agents and models, continuous observability and validation, vendor and model provenance tracking, and curated evidence capture for audits. Choosing and integrating these tools depends on industry risk profile, regulatory requirements and the complexity of agent architectures. The goal is not hype but repeatable, auditable verification processes that map technical controls to compliance obligations.